knitr::opts_chunk$set(
echo = FALSE, message = FALSE, warning = FALSE,
fig.width = 7, fig.height = 4, fig.retina = 2
)
options(knitr.kable.NA = "")
suppressPackageStartupMessages({
pacman::p_load(dplyr, tidyr, ggplot2, tibble, knitr, kableExtra)
})
root <- ".../projects/abcd-projs/smri-pub-abcd/"
source("longComBat-pub-sMRI-abcd.R")
log_file <- "render_progress.log"
writeLines("", log_file)
pipelines
thickness — f
[longCombat] found 5 batches [longCombat] found 71 features
[longCombat] found 6004 total observations [longCombat] standardizing
data across features… [longCombat] fitting lme model for feature 1
[longCombat] fitting lme model for feature 2 [longCombat] fitting lme
model for feature 3 [longCombat] fitting lme model for feature 4
[longCombat] fitting lme model for feature 5 [longCombat] fitting lme
model for feature 6 [longCombat] fitting lme model for feature 7
[longCombat] fitting lme model for feature 8 [longCombat] fitting lme
model for feature 9 [longCombat] fitting lme model for feature 10
[longCombat] fitting lme model for feature 11 [longCombat] fitting lme
model for feature 12 [longCombat] fitting lme model for feature 13
[longCombat] fitting lme model for feature 14 [longCombat] fitting lme
model for feature 15 [longCombat] fitting lme model for feature 16
[longCombat] fitting lme model for feature 17 [longCombat] fitting lme
model for feature 18 [longCombat] fitting lme model for feature 19
[longCombat] fitting lme model for feature 20 [longCombat] fitting lme
model for feature 21 [longCombat] fitting lme model for feature 22
[longCombat] fitting lme model for feature 23 [longCombat] fitting lme
model for feature 24 [longCombat] fitting lme model for feature 25
[longCombat] fitting lme model for feature 26 [longCombat] fitting lme
model for feature 27 [longCombat] fitting lme model for feature 28
[longCombat] fitting lme model for feature 29 [longCombat] fitting lme
model for feature 30 [longCombat] fitting lme model for feature 31
[longCombat] fitting lme model for feature 32 [longCombat] fitting lme
model for feature 33 [longCombat] fitting lme model for feature 34
[longCombat] fitting lme model for feature 35 [longCombat] fitting lme
model for feature 36 [longCombat] fitting lme model for feature 37
[longCombat] fitting lme model for feature 38 [longCombat] fitting lme
model for feature 39 [longCombat] fitting lme model for feature 40
[longCombat] fitting lme model for feature 41 [longCombat] fitting lme
model for feature 42 [longCombat] fitting lme model for feature 43
[longCombat] fitting lme model for feature 44 [longCombat] fitting lme
model for feature 45 [longCombat] fitting lme model for feature 46
[longCombat] fitting lme model for feature 47 [longCombat] fitting lme
model for feature 48 [longCombat] fitting lme model for feature 49
[longCombat] fitting lme model for feature 50 [longCombat] fitting lme
model for feature 51 [longCombat] fitting lme model for feature 52
[longCombat] fitting lme model for feature 53 [longCombat] fitting lme
model for feature 54 [longCombat] fitting lme model for feature 55
[longCombat] fitting lme model for feature 56 [longCombat] fitting lme
model for feature 57 [longCombat] fitting lme model for feature 58
[longCombat] fitting lme model for feature 59 [longCombat] fitting lme
model for feature 60 [longCombat] fitting lme model for feature 61
[longCombat] fitting lme model for feature 62 [longCombat] fitting lme
model for feature 63 [longCombat] fitting lme model for feature 64
[longCombat] fitting lme model for feature 65 [longCombat] fitting lme
model for feature 66 [longCombat] fitting lme model for feature 67
[longCombat] fitting lme model for feature 68 [longCombat] fitting lme
model for feature 69 [longCombat] fitting lme model for feature 70
[longCombat] fitting lme model for feature 71 [longCombat] using method
of moments to estimate hyperparameters [longCombat] using empirical
Bayes to estimate batch effects… [longCombat] initializing… [longCombat]
starting EM algorithm iteration 1 [longCombat] starting EM algorithm
iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat]
starting EM algorithm iteration 4 [longCombat] starting EM algorithm
iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat]
starting EM algorithm iteration 7 [longCombat] starting EM algorithm
iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat]
starting EM algorithm iteration 10 [longCombat] starting EM algorithm
iteration 11 [longCombat] starting EM algorithm iteration 12
[longCombat] starting EM algorithm iteration 13 [longCombat] starting EM
algorithm iteration 14 [longCombat] starting EM algorithm iteration 15
[longCombat] starting EM algorithm iteration 16 [longCombat] starting EM
algorithm iteration 17 [longCombat] starting EM algorithm iteration 18
[longCombat] starting EM algorithm iteration 19 [longCombat] starting EM
algorithm iteration 20 [longCombat] starting EM algorithm iteration 21
[longCombat] starting EM algorithm iteration 22 [longCombat] starting EM
algorithm iteration 23 [longCombat] starting EM algorithm iteration 24
[longCombat] starting EM algorithm iteration 25 [longCombat] starting EM
algorithm iteration 26 [longCombat] starting EM algorithm iteration 27
[longCombat] starting EM algorithm iteration 28 [longCombat] starting EM
algorithm iteration 29 [longCombat] starting EM algorithm iteration 30
[longCombat] adjusting data for batch effects [longCombat] found 5
batches [longCombat] found 71 features [longCombat] found 6004 total
observations [longCombat] standardizing data across features…
[longCombat] fitting lme model for feature 1 [longCombat] fitting lme
model for feature 2 [longCombat] fitting lme model for feature 3
[longCombat] fitting lme model for feature 4 [longCombat] fitting lme
model for feature 5 [longCombat] fitting lme model for feature 6
[longCombat] fitting lme model for feature 7 [longCombat] fitting lme
model for feature 8 [longCombat] fitting lme model for feature 9
[longCombat] fitting lme model for feature 10 [longCombat] fitting lme
model for feature 11 [longCombat] fitting lme model for feature 12
[longCombat] fitting lme model for feature 13 [longCombat] fitting lme
model for feature 14 [longCombat] fitting lme model for feature 15
[longCombat] fitting lme model for feature 16 [longCombat] fitting lme
model for feature 17 [longCombat] fitting lme model for feature 18
[longCombat] fitting lme model for feature 19 [longCombat] fitting lme
model for feature 20 [longCombat] fitting lme model for feature 21
[longCombat] fitting lme model for feature 22 [longCombat] fitting lme
model for feature 23 [longCombat] fitting lme model for feature 24
[longCombat] fitting lme model for feature 25 [longCombat] fitting lme
model for feature 26 [longCombat] fitting lme model for feature 27
[longCombat] fitting lme model for feature 28 [longCombat] fitting lme
model for feature 29 [longCombat] fitting lme model for feature 30
[longCombat] fitting lme model for feature 31 [longCombat] fitting lme
model for feature 32 [longCombat] fitting lme model for feature 33
[longCombat] fitting lme model for feature 34 [longCombat] fitting lme
model for feature 35 [longCombat] fitting lme model for feature 36
[longCombat] fitting lme model for feature 37 [longCombat] fitting lme
model for feature 38 [longCombat] fitting lme model for feature 39
[longCombat] fitting lme model for feature 40 [longCombat] fitting lme
model for feature 41 [longCombat] fitting lme model for feature 42
[longCombat] fitting lme model for feature 43 [longCombat] fitting lme
model for feature 44 [longCombat] fitting lme model for feature 45
[longCombat] fitting lme model for feature 46 [longCombat] fitting lme
model for feature 47 [longCombat] fitting lme model for feature 48
[longCombat] fitting lme model for feature 49 [longCombat] fitting lme
model for feature 50 [longCombat] fitting lme model for feature 51
[longCombat] fitting lme model for feature 52 [longCombat] fitting lme
model for feature 53 [longCombat] fitting lme model for feature 54
[longCombat] fitting lme model for feature 55 [longCombat] fitting lme
model for feature 56 [longCombat] fitting lme model for feature 57
[longCombat] fitting lme model for feature 58 [longCombat] fitting lme
model for feature 59 [longCombat] fitting lme model for feature 60
[longCombat] fitting lme model for feature 61 [longCombat] fitting lme
model for feature 62 [longCombat] fitting lme model for feature 63
[longCombat] fitting lme model for feature 64 [longCombat] fitting lme
model for feature 65 [longCombat] fitting lme model for feature 66
[longCombat] fitting lme model for feature 67 [longCombat] fitting lme
model for feature 68 [longCombat] fitting lme model for feature 69
[longCombat] fitting lme model for feature 70 [longCombat] fitting lme
model for feature 71 [longCombat] using method of moments to estimate
hyperparameters [longCombat] using empirical Bayes to estimate batch
effects… [longCombat] initializing… [longCombat] starting EM algorithm
iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat]
starting EM algorithm iteration 3 [longCombat] starting EM algorithm
iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat]
starting EM algorithm iteration 6 [longCombat] starting EM algorithm
iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat]
starting EM algorithm iteration 9 [longCombat] starting EM algorithm
iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
71 features [longCombat] found 6004 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] fitting lme model for
feature 43 [longCombat] fitting lme model for feature 44 [longCombat]
fitting lme model for feature 45 [longCombat] fitting lme model for
feature 46 [longCombat] fitting lme model for feature 47 [longCombat]
fitting lme model for feature 48 [longCombat] fitting lme model for
feature 49 [longCombat] fitting lme model for feature 50 [longCombat]
fitting lme model for feature 51 [longCombat] fitting lme model for
feature 52 [longCombat] fitting lme model for feature 53 [longCombat]
fitting lme model for feature 54 [longCombat] fitting lme model for
feature 55 [longCombat] fitting lme model for feature 56 [longCombat]
fitting lme model for feature 57 [longCombat] fitting lme model for
feature 58 [longCombat] fitting lme model for feature 59 [longCombat]
fitting lme model for feature 60 [longCombat] fitting lme model for
feature 61 [longCombat] fitting lme model for feature 62 [longCombat]
fitting lme model for feature 63 [longCombat] fitting lme model for
feature 64 [longCombat] fitting lme model for feature 65 [longCombat]
fitting lme model for feature 66 [longCombat] fitting lme model for
feature 67 [longCombat] fitting lme model for feature 68 [longCombat]
fitting lme model for feature 69 [longCombat] fitting lme model for
feature 70 [longCombat] fitting lme model for feature 71 [longCombat]
using method of moments to estimate hyperparameters [longCombat] using
empirical Bayes to estimate batch effects… [longCombat] initializing…
[longCombat] starting EM algorithm iteration 1 [longCombat] starting EM
algorithm iteration 2 [longCombat] starting EM algorithm iteration 3
[longCombat] starting EM algorithm iteration 4 [longCombat] starting EM
algorithm iteration 5 [longCombat] starting EM algorithm iteration 6
[longCombat] starting EM algorithm iteration 7 [longCombat] starting EM
algorithm iteration 8 [longCombat] starting EM algorithm iteration 9
[longCombat] starting EM algorithm iteration 10 [longCombat] starting EM
algorithm iteration 11 [longCombat] starting EM algorithm iteration 12
[longCombat] starting EM algorithm iteration 13 [longCombat] starting EM
algorithm iteration 14 [longCombat] starting EM algorithm iteration 15
[longCombat] starting EM algorithm iteration 16 [longCombat] starting EM
algorithm iteration 17 [longCombat] starting EM algorithm iteration 18
[longCombat] starting EM algorithm iteration 19 [longCombat] starting EM
algorithm iteration 20 [longCombat] starting EM algorithm iteration 21
[longCombat] starting EM algorithm iteration 22 [longCombat] starting EM
algorithm iteration 23 [longCombat] starting EM algorithm iteration 24
[longCombat] starting EM algorithm iteration 25 [longCombat] starting EM
algorithm iteration 26 [longCombat] starting EM algorithm iteration 27
[longCombat] starting EM algorithm iteration 28 [longCombat] starting EM
algorithm iteration 29 [longCombat] starting EM algorithm iteration 30
[longCombat] adjusting data for batch effects [longCombat] found 5
batches [longCombat] found 71 features [longCombat] found 6004 total
observations [longCombat] standardizing data across features…
[longCombat] fitting lme model for feature 1 [longCombat] fitting lme
model for feature 2 [longCombat] fitting lme model for feature 3
[longCombat] fitting lme model for feature 4 [longCombat] fitting lme
model for feature 5 [longCombat] fitting lme model for feature 6
[longCombat] fitting lme model for feature 7 [longCombat] fitting lme
model for feature 8 [longCombat] fitting lme model for feature 9
[longCombat] fitting lme model for feature 10 [longCombat] fitting lme
model for feature 11 [longCombat] fitting lme model for feature 12
[longCombat] fitting lme model for feature 13 [longCombat] fitting lme
model for feature 14 [longCombat] fitting lme model for feature 15
[longCombat] fitting lme model for feature 16 [longCombat] fitting lme
model for feature 17 [longCombat] fitting lme model for feature 18
[longCombat] fitting lme model for feature 19 [longCombat] fitting lme
model for feature 20 [longCombat] fitting lme model for feature 21
[longCombat] fitting lme model for feature 22 [longCombat] fitting lme
model for feature 23 [longCombat] fitting lme model for feature 24
[longCombat] fitting lme model for feature 25 [longCombat] fitting lme
model for feature 26 [longCombat] fitting lme model for feature 27
[longCombat] fitting lme model for feature 28 [longCombat] fitting lme
model for feature 29 [longCombat] fitting lme model for feature 30
[longCombat] fitting lme model for feature 31 [longCombat] fitting lme
model for feature 32 [longCombat] fitting lme model for feature 33
[longCombat] fitting lme model for feature 34 [longCombat] fitting lme
model for feature 35 [longCombat] fitting lme model for feature 36
[longCombat] fitting lme model for feature 37 [longCombat] fitting lme
model for feature 38 [longCombat] fitting lme model for feature 39
[longCombat] fitting lme model for feature 40 [longCombat] fitting lme
model for feature 41 [longCombat] fitting lme model for feature 42
[longCombat] fitting lme model for feature 43 [longCombat] fitting lme
model for feature 44 [longCombat] fitting lme model for feature 45
[longCombat] fitting lme model for feature 46 [longCombat] fitting lme
model for feature 47 [longCombat] fitting lme model for feature 48
[longCombat] fitting lme model for feature 49 [longCombat] fitting lme
model for feature 50 [longCombat] fitting lme model for feature 51
[longCombat] fitting lme model for feature 52 [longCombat] fitting lme
model for feature 53 [longCombat] fitting lme model for feature 54
[longCombat] fitting lme model for feature 55 [longCombat] fitting lme
model for feature 56 [longCombat] fitting lme model for feature 57
[longCombat] fitting lme model for feature 58 [longCombat] fitting lme
model for feature 59 [longCombat] fitting lme model for feature 60
[longCombat] fitting lme model for feature 61 [longCombat] fitting lme
model for feature 62 [longCombat] fitting lme model for feature 63
[longCombat] fitting lme model for feature 64 [longCombat] fitting lme
model for feature 65 [longCombat] fitting lme model for feature 66
[longCombat] fitting lme model for feature 67 [longCombat] fitting lme
model for feature 68 [longCombat] fitting lme model for feature 69
[longCombat] fitting lme model for feature 70 [longCombat] fitting lme
model for feature 71 [longCombat] using method of moments to estimate
hyperparameters [longCombat] using empirical Bayes to estimate batch
effects… [longCombat] initializing… [longCombat] starting EM algorithm
iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat]
starting EM algorithm iteration 3 [longCombat] starting EM algorithm
iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat]
starting EM algorithm iteration 6 [longCombat] starting EM algorithm
iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat]
starting EM algorithm iteration 9 [longCombat] starting EM algorithm
iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
71 features [longCombat] found 6004 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] fitting lme model for
feature 43 [longCombat] fitting lme model for feature 44 [longCombat]
fitting lme model for feature 45 [longCombat] fitting lme model for
feature 46 [longCombat] fitting lme model for feature 47 [longCombat]
fitting lme model for feature 48 [longCombat] fitting lme model for
feature 49 [longCombat] fitting lme model for feature 50 [longCombat]
fitting lme model for feature 51 [longCombat] fitting lme model for
feature 52 [longCombat] fitting lme model for feature 53 [longCombat]
fitting lme model for feature 54 [longCombat] fitting lme model for
feature 55 [longCombat] fitting lme model for feature 56 [longCombat]
fitting lme model for feature 57 [longCombat] fitting lme model for
feature 58 [longCombat] fitting lme model for feature 59 [longCombat]
fitting lme model for feature 60 [longCombat] fitting lme model for
feature 61 [longCombat] fitting lme model for feature 62 [longCombat]
fitting lme model for feature 63 [longCombat] fitting lme model for
feature 64 [longCombat] fitting lme model for feature 65 [longCombat]
fitting lme model for feature 66 [longCombat] fitting lme model for
feature 67 [longCombat] fitting lme model for feature 68 [longCombat]
fitting lme model for feature 69 [longCombat] fitting lme model for
feature 70 [longCombat] fitting lme model for feature 71 [longCombat]
using method of moments to estimate hyperparameters [longCombat] using
empirical Bayes to estimate batch effects… [longCombat] initializing…
[longCombat] starting EM algorithm iteration 1 [longCombat] starting EM
algorithm iteration 2 [longCombat] starting EM algorithm iteration 3
[longCombat] starting EM algorithm iteration 4 [longCombat] starting EM
algorithm iteration 5 [longCombat] starting EM algorithm iteration 6
[longCombat] starting EM algorithm iteration 7 [longCombat] starting EM
algorithm iteration 8 [longCombat] starting EM algorithm iteration 9
[longCombat] starting EM algorithm iteration 10 [longCombat] starting EM
algorithm iteration 11 [longCombat] starting EM algorithm iteration 12
[longCombat] starting EM algorithm iteration 13 [longCombat] starting EM
algorithm iteration 14 [longCombat] starting EM algorithm iteration 15
[longCombat] starting EM algorithm iteration 16 [longCombat] starting EM
algorithm iteration 17 [longCombat] starting EM algorithm iteration 18
[longCombat] starting EM algorithm iteration 19 [longCombat] starting EM
algorithm iteration 20 [longCombat] starting EM algorithm iteration 21
[longCombat] starting EM algorithm iteration 22 [longCombat] starting EM
algorithm iteration 23 [longCombat] starting EM algorithm iteration 24
[longCombat] starting EM algorithm iteration 25 [longCombat] starting EM
algorithm iteration 26 [longCombat] starting EM algorithm iteration 27
[longCombat] starting EM algorithm iteration 28 [longCombat] starting EM
algorithm iteration 29 [longCombat] starting EM algorithm iteration 30
[longCombat] adjusting data for batch effects
Harmonization Model: age
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6
dataset: thickness | sex: f |
region: smri_thick_cdk_cdmdfrlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-11028.53
|
-11001.73
|
5518.266
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-11101.46
|
-11047.86
|
5558.731
|
1 vs 2
|
80.93027
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-9208.909
|
-9182.108
|
4608.454
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-9200.964
|
-9147.363
|
4608.482
|
1 vs 2
|
0.0555966
|
0.9996207
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
2.813
|
0.118
|
|
brain_metric
|
scanner2
|
1720
|
2.845
|
0.137
|
|
brain_metric
|
scanner3
|
204
|
2.780
|
0.118
|
|
brain_metric
|
scanner5
|
1596
|
2.854
|
0.115
|
|
brain_metric
|
scanner6
|
2015
|
2.859
|
0.119
|
|
brain_metric.combat
|
scanner1
|
469
|
2.854
|
0.127
|
|
brain_metric.combat
|
scanner2
|
1720
|
2.848
|
0.136
|
|
brain_metric.combat
|
scanner3
|
204
|
2.844
|
0.122
|
|
brain_metric.combat
|
scanner5
|
1596
|
2.842
|
0.133
|
|
brain_metric.combat
|
scanner6
|
2015
|
2.850
|
0.136
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-0.023
|
|
|
|
|
age_slope
|
|
-0.023
|
|
|
|
scanner:scanner3
|
2.783
|
2.847
|
0.064
|
2.297
|
|
scanner:scanner1
|
2.806
|
2.847
|
0.041
|
1.474
|
|
scanner:scanner5
|
2.859
|
2.847
|
-0.012
|
-0.417
|
|
scanner:scanner6
|
2.857
|
2.848
|
-0.009
|
-0.310
|
|
scanner:scanner2
|
2.845
|
2.848
|
0.003
|
0.090
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_timing
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled
dataset: thickness | sex: f |
region: smri_thick_cdk_cdmdfrlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-11028.53
|
-11001.73
|
5518.266
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-11101.46
|
-11047.86
|
5558.731
|
1 vs 2
|
80.93027
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-9210.049
|
-9183.248
|
4609.024
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-9202.094
|
-9148.493
|
4609.047
|
1 vs 2
|
0.045162
|
0.9997489
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
2.813
|
0.118
|
|
brain_metric
|
scanner2
|
1720
|
2.845
|
0.137
|
|
brain_metric
|
scanner3
|
204
|
2.780
|
0.118
|
|
brain_metric
|
scanner5
|
1596
|
2.854
|
0.115
|
|
brain_metric
|
scanner6
|
2015
|
2.859
|
0.119
|
|
brain_metric.combat
|
scanner1
|
469
|
2.854
|
0.127
|
|
brain_metric.combat
|
scanner2
|
1720
|
2.847
|
0.136
|
|
brain_metric.combat
|
scanner3
|
204
|
2.843
|
0.122
|
|
brain_metric.combat
|
scanner5
|
1596
|
2.842
|
0.133
|
|
brain_metric.combat
|
scanner6
|
2015
|
2.850
|
0.136
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-0.023
|
|
|
|
|
age_slope
|
|
-0.023
|
|
|
|
scanner:scanner3
|
2.783
|
2.846
|
0.063
|
2.268
|
|
scanner:scanner1
|
2.806
|
2.848
|
0.042
|
1.490
|
|
scanner:scanner5
|
2.859
|
2.847
|
-0.012
|
-0.410
|
|
scanner:scanner6
|
2.857
|
2.848
|
-0.009
|
-0.314
|
|
scanner:scanner2
|
2.845
|
2.848
|
0.002
|
0.087
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_tempo
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled
dataset: thickness | sex: f |
region: smri_thick_cdk_cdmdfrlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-11028.53
|
-11001.73
|
5518.266
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-11101.46
|
-11047.86
|
5558.731
|
1 vs 2
|
80.93027
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-9208.994
|
-9182.193
|
4608.497
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-9201.053
|
-9147.452
|
4608.527
|
1 vs 2
|
0.059926
|
0.99956
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
2.813
|
0.118
|
|
brain_metric
|
scanner2
|
1720
|
2.845
|
0.137
|
|
brain_metric
|
scanner3
|
204
|
2.780
|
0.118
|
|
brain_metric
|
scanner5
|
1596
|
2.854
|
0.115
|
|
brain_metric
|
scanner6
|
2015
|
2.859
|
0.119
|
|
brain_metric.combat
|
scanner1
|
469
|
2.854
|
0.127
|
|
brain_metric.combat
|
scanner2
|
1720
|
2.848
|
0.136
|
|
brain_metric.combat
|
scanner3
|
204
|
2.843
|
0.122
|
|
brain_metric.combat
|
scanner5
|
1596
|
2.842
|
0.133
|
|
brain_metric.combat
|
scanner6
|
2015
|
2.850
|
0.136
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-0.023
|
|
|
|
|
age_slope
|
|
-0.023
|
|
|
|
scanner:scanner3
|
2.783
|
2.846
|
0.064
|
2.284
|
|
scanner:scanner1
|
2.806
|
2.848
|
0.042
|
1.484
|
|
scanner:scanner5
|
2.859
|
2.847
|
-0.012
|
-0.417
|
|
scanner:scanner6
|
2.857
|
2.848
|
-0.009
|
-0.310
|
|
scanner:scanner2
|
2.845
|
2.848
|
0.003
|
0.088
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_timing_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 +
s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 +
s_age_by_timing_6 + s_age_by_timing_7
dataset: thickness | sex: f |
region: smri_thick_cdk_cdmdfrlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-11028.53
|
-11001.73
|
5518.266
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-11101.46
|
-11047.86
|
5558.731
|
1 vs 2
|
80.93027
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-9212.788
|
-9185.987
|
4610.394
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-9204.859
|
-9151.258
|
4610.430
|
1 vs 2
|
0.0714682
|
0.9993765
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
2.813
|
0.118
|
|
brain_metric
|
scanner2
|
1720
|
2.845
|
0.137
|
|
brain_metric
|
scanner3
|
204
|
2.780
|
0.118
|
|
brain_metric
|
scanner5
|
1596
|
2.854
|
0.115
|
|
brain_metric
|
scanner6
|
2015
|
2.859
|
0.119
|
|
brain_metric.combat
|
scanner1
|
469
|
2.854
|
0.127
|
|
brain_metric.combat
|
scanner2
|
1720
|
2.848
|
0.136
|
|
brain_metric.combat
|
scanner3
|
204
|
2.843
|
0.122
|
|
brain_metric.combat
|
scanner5
|
1596
|
2.842
|
0.133
|
|
brain_metric.combat
|
scanner6
|
2015
|
2.850
|
0.136
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-0.023
|
|
|
|
|
age_slope
|
|
-0.023
|
|
|
|
scanner:scanner3
|
2.783
|
2.846
|
0.063
|
2.266
|
|
scanner:scanner1
|
2.806
|
2.848
|
0.042
|
1.485
|
|
scanner:scanner5
|
2.859
|
2.847
|
-0.012
|
-0.416
|
|
scanner:scanner6
|
2.857
|
2.848
|
-0.009
|
-0.309
|
|
scanner:scanner2
|
2.845
|
2.848
|
0.003
|
0.089
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_tempo_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 +
s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 +
s_age_by_tempo_6 + s_age_by_tempo_7
dataset: thickness | sex: f |
region: smri_thick_cdk_cdmdfrlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-11028.53
|
-11001.73
|
5518.266
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-11101.46
|
-11047.86
|
5558.731
|
1 vs 2
|
80.93027
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-9204.983
|
-9178.182
|
4606.491
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-9197.041
|
-9143.440
|
4606.521
|
1 vs 2
|
0.0587585
|
0.9995768
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
2.813
|
0.118
|
|
brain_metric
|
scanner2
|
1720
|
2.845
|
0.137
|
|
brain_metric
|
scanner3
|
204
|
2.780
|
0.118
|
|
brain_metric
|
scanner5
|
1596
|
2.854
|
0.115
|
|
brain_metric
|
scanner6
|
2015
|
2.859
|
0.119
|
|
brain_metric.combat
|
scanner1
|
469
|
2.854
|
0.127
|
|
brain_metric.combat
|
scanner2
|
1720
|
2.847
|
0.136
|
|
brain_metric.combat
|
scanner3
|
204
|
2.843
|
0.122
|
|
brain_metric.combat
|
scanner5
|
1596
|
2.842
|
0.133
|
|
brain_metric.combat
|
scanner6
|
2015
|
2.850
|
0.136
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-0.023
|
|
|
|
|
age_slope
|
|
-0.023
|
|
|
|
scanner:scanner3
|
2.783
|
2.846
|
0.064
|
2.282
|
|
scanner:scanner1
|
2.806
|
2.848
|
0.042
|
1.484
|
|
scanner:scanner5
|
2.859
|
2.847
|
-0.012
|
-0.416
|
|
scanner:scanner6
|
2.857
|
2.848
|
-0.009
|
-0.310
|
|
scanner:scanner2
|
2.845
|
2.848
|
0.002
|
0.088
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
thickness — m
[longCombat] found 5 batches [longCombat] found 71 features
[longCombat] found 6863 total observations [longCombat] standardizing
data across features… [longCombat] fitting lme model for feature 1
[longCombat] fitting lme model for feature 2 [longCombat] fitting lme
model for feature 3 [longCombat] fitting lme model for feature 4
[longCombat] fitting lme model for feature 5 [longCombat] fitting lme
model for feature 6 [longCombat] fitting lme model for feature 7
[longCombat] fitting lme model for feature 8 [longCombat] fitting lme
model for feature 9 [longCombat] fitting lme model for feature 10
[longCombat] fitting lme model for feature 11 [longCombat] fitting lme
model for feature 12 [longCombat] fitting lme model for feature 13
[longCombat] fitting lme model for feature 14 [longCombat] fitting lme
model for feature 15 [longCombat] fitting lme model for feature 16
[longCombat] fitting lme model for feature 17 [longCombat] fitting lme
model for feature 18 [longCombat] fitting lme model for feature 19
[longCombat] fitting lme model for feature 20 [longCombat] fitting lme
model for feature 21 [longCombat] fitting lme model for feature 22
[longCombat] fitting lme model for feature 23 [longCombat] fitting lme
model for feature 24 [longCombat] fitting lme model for feature 25
[longCombat] fitting lme model for feature 26 [longCombat] fitting lme
model for feature 27 [longCombat] fitting lme model for feature 28
[longCombat] fitting lme model for feature 29 [longCombat] fitting lme
model for feature 30 [longCombat] fitting lme model for feature 31
[longCombat] fitting lme model for feature 32 [longCombat] fitting lme
model for feature 33 [longCombat] fitting lme model for feature 34
[longCombat] fitting lme model for feature 35 [longCombat] fitting lme
model for feature 36 [longCombat] fitting lme model for feature 37
[longCombat] fitting lme model for feature 38 [longCombat] fitting lme
model for feature 39 [longCombat] fitting lme model for feature 40
[longCombat] fitting lme model for feature 41 [longCombat] fitting lme
model for feature 42 [longCombat] fitting lme model for feature 43
[longCombat] fitting lme model for feature 44 [longCombat] fitting lme
model for feature 45 [longCombat] fitting lme model for feature 46
[longCombat] fitting lme model for feature 47 [longCombat] fitting lme
model for feature 48 [longCombat] fitting lme model for feature 49
[longCombat] fitting lme model for feature 50 [longCombat] fitting lme
model for feature 51 [longCombat] fitting lme model for feature 52
[longCombat] fitting lme model for feature 53 [longCombat] fitting lme
model for feature 54 [longCombat] fitting lme model for feature 55
[longCombat] fitting lme model for feature 56 [longCombat] fitting lme
model for feature 57 [longCombat] fitting lme model for feature 58
[longCombat] fitting lme model for feature 59 [longCombat] fitting lme
model for feature 60 [longCombat] fitting lme model for feature 61
[longCombat] fitting lme model for feature 62 [longCombat] fitting lme
model for feature 63 [longCombat] fitting lme model for feature 64
[longCombat] fitting lme model for feature 65 [longCombat] fitting lme
model for feature 66 [longCombat] fitting lme model for feature 67
[longCombat] fitting lme model for feature 68 [longCombat] fitting lme
model for feature 69 [longCombat] fitting lme model for feature 70
[longCombat] fitting lme model for feature 71 [longCombat] using method
of moments to estimate hyperparameters [longCombat] using empirical
Bayes to estimate batch effects… [longCombat] initializing… [longCombat]
starting EM algorithm iteration 1 [longCombat] starting EM algorithm
iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat]
starting EM algorithm iteration 4 [longCombat] starting EM algorithm
iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat]
starting EM algorithm iteration 7 [longCombat] starting EM algorithm
iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat]
starting EM algorithm iteration 10 [longCombat] starting EM algorithm
iteration 11 [longCombat] starting EM algorithm iteration 12
[longCombat] starting EM algorithm iteration 13 [longCombat] starting EM
algorithm iteration 14 [longCombat] starting EM algorithm iteration 15
[longCombat] starting EM algorithm iteration 16 [longCombat] starting EM
algorithm iteration 17 [longCombat] starting EM algorithm iteration 18
[longCombat] starting EM algorithm iteration 19 [longCombat] starting EM
algorithm iteration 20 [longCombat] starting EM algorithm iteration 21
[longCombat] starting EM algorithm iteration 22 [longCombat] starting EM
algorithm iteration 23 [longCombat] starting EM algorithm iteration 24
[longCombat] starting EM algorithm iteration 25 [longCombat] starting EM
algorithm iteration 26 [longCombat] starting EM algorithm iteration 27
[longCombat] starting EM algorithm iteration 28 [longCombat] starting EM
algorithm iteration 29 [longCombat] starting EM algorithm iteration 30
[longCombat] adjusting data for batch effects [longCombat] found 5
batches [longCombat] found 71 features [longCombat] found 6863 total
observations [longCombat] standardizing data across features…
[longCombat] fitting lme model for feature 1 [longCombat] fitting lme
model for feature 2 [longCombat] fitting lme model for feature 3
[longCombat] fitting lme model for feature 4 [longCombat] fitting lme
model for feature 5 [longCombat] fitting lme model for feature 6
[longCombat] fitting lme model for feature 7 [longCombat] fitting lme
model for feature 8 [longCombat] fitting lme model for feature 9
[longCombat] fitting lme model for feature 10 [longCombat] fitting lme
model for feature 11 [longCombat] fitting lme model for feature 12
[longCombat] fitting lme model for feature 13 [longCombat] fitting lme
model for feature 14 [longCombat] fitting lme model for feature 15
[longCombat] fitting lme model for feature 16 [longCombat] fitting lme
model for feature 17 [longCombat] fitting lme model for feature 18
[longCombat] fitting lme model for feature 19 [longCombat] fitting lme
model for feature 20 [longCombat] fitting lme model for feature 21
[longCombat] fitting lme model for feature 22 [longCombat] fitting lme
model for feature 23 [longCombat] fitting lme model for feature 24
[longCombat] fitting lme model for feature 25 [longCombat] fitting lme
model for feature 26 [longCombat] fitting lme model for feature 27
[longCombat] fitting lme model for feature 28 [longCombat] fitting lme
model for feature 29 [longCombat] fitting lme model for feature 30
[longCombat] fitting lme model for feature 31 [longCombat] fitting lme
model for feature 32 [longCombat] fitting lme model for feature 33
[longCombat] fitting lme model for feature 34 [longCombat] fitting lme
model for feature 35 [longCombat] fitting lme model for feature 36
[longCombat] fitting lme model for feature 37 [longCombat] fitting lme
model for feature 38 [longCombat] fitting lme model for feature 39
[longCombat] fitting lme model for feature 40 [longCombat] fitting lme
model for feature 41 [longCombat] fitting lme model for feature 42
[longCombat] fitting lme model for feature 43 [longCombat] fitting lme
model for feature 44 [longCombat] fitting lme model for feature 45
[longCombat] fitting lme model for feature 46 [longCombat] fitting lme
model for feature 47 [longCombat] fitting lme model for feature 48
[longCombat] fitting lme model for feature 49 [longCombat] fitting lme
model for feature 50 [longCombat] fitting lme model for feature 51
[longCombat] fitting lme model for feature 52 [longCombat] fitting lme
model for feature 53 [longCombat] fitting lme model for feature 54
[longCombat] fitting lme model for feature 55 [longCombat] fitting lme
model for feature 56 [longCombat] fitting lme model for feature 57
[longCombat] fitting lme model for feature 58 [longCombat] fitting lme
model for feature 59 [longCombat] fitting lme model for feature 60
[longCombat] fitting lme model for feature 61 [longCombat] fitting lme
model for feature 62 [longCombat] fitting lme model for feature 63
[longCombat] fitting lme model for feature 64 [longCombat] fitting lme
model for feature 65 [longCombat] fitting lme model for feature 66
[longCombat] fitting lme model for feature 67 [longCombat] fitting lme
model for feature 68 [longCombat] fitting lme model for feature 69
[longCombat] fitting lme model for feature 70 [longCombat] fitting lme
model for feature 71 [longCombat] using method of moments to estimate
hyperparameters [longCombat] using empirical Bayes to estimate batch
effects… [longCombat] initializing… [longCombat] starting EM algorithm
iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat]
starting EM algorithm iteration 3 [longCombat] starting EM algorithm
iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat]
starting EM algorithm iteration 6 [longCombat] starting EM algorithm
iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat]
starting EM algorithm iteration 9 [longCombat] starting EM algorithm
iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
71 features [longCombat] found 6863 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] fitting lme model for
feature 43 [longCombat] fitting lme model for feature 44 [longCombat]
fitting lme model for feature 45 [longCombat] fitting lme model for
feature 46 [longCombat] fitting lme model for feature 47 [longCombat]
fitting lme model for feature 48 [longCombat] fitting lme model for
feature 49 [longCombat] fitting lme model for feature 50 [longCombat]
fitting lme model for feature 51 [longCombat] fitting lme model for
feature 52 [longCombat] fitting lme model for feature 53 [longCombat]
fitting lme model for feature 54 [longCombat] fitting lme model for
feature 55 [longCombat] fitting lme model for feature 56 [longCombat]
fitting lme model for feature 57 [longCombat] fitting lme model for
feature 58 [longCombat] fitting lme model for feature 59 [longCombat]
fitting lme model for feature 60 [longCombat] fitting lme model for
feature 61 [longCombat] fitting lme model for feature 62 [longCombat]
fitting lme model for feature 63 [longCombat] fitting lme model for
feature 64 [longCombat] fitting lme model for feature 65 [longCombat]
fitting lme model for feature 66 [longCombat] fitting lme model for
feature 67 [longCombat] fitting lme model for feature 68 [longCombat]
fitting lme model for feature 69 [longCombat] fitting lme model for
feature 70 [longCombat] fitting lme model for feature 71 [longCombat]
using method of moments to estimate hyperparameters [longCombat] using
empirical Bayes to estimate batch effects… [longCombat] initializing…
[longCombat] starting EM algorithm iteration 1 [longCombat] starting EM
algorithm iteration 2 [longCombat] starting EM algorithm iteration 3
[longCombat] starting EM algorithm iteration 4 [longCombat] starting EM
algorithm iteration 5 [longCombat] starting EM algorithm iteration 6
[longCombat] starting EM algorithm iteration 7 [longCombat] starting EM
algorithm iteration 8 [longCombat] starting EM algorithm iteration 9
[longCombat] starting EM algorithm iteration 10 [longCombat] starting EM
algorithm iteration 11 [longCombat] starting EM algorithm iteration 12
[longCombat] starting EM algorithm iteration 13 [longCombat] starting EM
algorithm iteration 14 [longCombat] starting EM algorithm iteration 15
[longCombat] starting EM algorithm iteration 16 [longCombat] starting EM
algorithm iteration 17 [longCombat] starting EM algorithm iteration 18
[longCombat] starting EM algorithm iteration 19 [longCombat] starting EM
algorithm iteration 20 [longCombat] starting EM algorithm iteration 21
[longCombat] starting EM algorithm iteration 22 [longCombat] starting EM
algorithm iteration 23 [longCombat] starting EM algorithm iteration 24
[longCombat] starting EM algorithm iteration 25 [longCombat] starting EM
algorithm iteration 26 [longCombat] starting EM algorithm iteration 27
[longCombat] starting EM algorithm iteration 28 [longCombat] starting EM
algorithm iteration 29 [longCombat] starting EM algorithm iteration 30
[longCombat] adjusting data for batch effects [longCombat] found 5
batches [longCombat] found 71 features [longCombat] found 6863 total
observations [longCombat] standardizing data across features…
[longCombat] fitting lme model for feature 1 [longCombat] fitting lme
model for feature 2 [longCombat] fitting lme model for feature 3
[longCombat] fitting lme model for feature 4 [longCombat] fitting lme
model for feature 5 [longCombat] fitting lme model for feature 6
[longCombat] fitting lme model for feature 7 [longCombat] fitting lme
model for feature 8 [longCombat] fitting lme model for feature 9
[longCombat] fitting lme model for feature 10 [longCombat] fitting lme
model for feature 11 [longCombat] fitting lme model for feature 12
[longCombat] fitting lme model for feature 13 [longCombat] fitting lme
model for feature 14 [longCombat] fitting lme model for feature 15
[longCombat] fitting lme model for feature 16 [longCombat] fitting lme
model for feature 17 [longCombat] fitting lme model for feature 18
[longCombat] fitting lme model for feature 19 [longCombat] fitting lme
model for feature 20 [longCombat] fitting lme model for feature 21
[longCombat] fitting lme model for feature 22 [longCombat] fitting lme
model for feature 23 [longCombat] fitting lme model for feature 24
[longCombat] fitting lme model for feature 25 [longCombat] fitting lme
model for feature 26 [longCombat] fitting lme model for feature 27
[longCombat] fitting lme model for feature 28 [longCombat] fitting lme
model for feature 29 [longCombat] fitting lme model for feature 30
[longCombat] fitting lme model for feature 31 [longCombat] fitting lme
model for feature 32 [longCombat] fitting lme model for feature 33
[longCombat] fitting lme model for feature 34 [longCombat] fitting lme
model for feature 35 [longCombat] fitting lme model for feature 36
[longCombat] fitting lme model for feature 37 [longCombat] fitting lme
model for feature 38 [longCombat] fitting lme model for feature 39
[longCombat] fitting lme model for feature 40 [longCombat] fitting lme
model for feature 41 [longCombat] fitting lme model for feature 42
[longCombat] fitting lme model for feature 43 [longCombat] fitting lme
model for feature 44 [longCombat] fitting lme model for feature 45
[longCombat] fitting lme model for feature 46 [longCombat] fitting lme
model for feature 47 [longCombat] fitting lme model for feature 48
[longCombat] fitting lme model for feature 49 [longCombat] fitting lme
model for feature 50 [longCombat] fitting lme model for feature 51
[longCombat] fitting lme model for feature 52 [longCombat] fitting lme
model for feature 53 [longCombat] fitting lme model for feature 54
[longCombat] fitting lme model for feature 55 [longCombat] fitting lme
model for feature 56 [longCombat] fitting lme model for feature 57
[longCombat] fitting lme model for feature 58 [longCombat] fitting lme
model for feature 59 [longCombat] fitting lme model for feature 60
[longCombat] fitting lme model for feature 61 [longCombat] fitting lme
model for feature 62 [longCombat] fitting lme model for feature 63
[longCombat] fitting lme model for feature 64 [longCombat] fitting lme
model for feature 65 [longCombat] fitting lme model for feature 66
[longCombat] fitting lme model for feature 67 [longCombat] fitting lme
model for feature 68 [longCombat] fitting lme model for feature 69
[longCombat] fitting lme model for feature 70 [longCombat] fitting lme
model for feature 71 [longCombat] using method of moments to estimate
hyperparameters [longCombat] using empirical Bayes to estimate batch
effects… [longCombat] initializing… [longCombat] starting EM algorithm
iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat]
starting EM algorithm iteration 3 [longCombat] starting EM algorithm
iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat]
starting EM algorithm iteration 6 [longCombat] starting EM algorithm
iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat]
starting EM algorithm iteration 9 [longCombat] starting EM algorithm
iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
71 features [longCombat] found 6863 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] fitting lme model for
feature 43 [longCombat] fitting lme model for feature 44 [longCombat]
fitting lme model for feature 45 [longCombat] fitting lme model for
feature 46 [longCombat] fitting lme model for feature 47 [longCombat]
fitting lme model for feature 48 [longCombat] fitting lme model for
feature 49 [longCombat] fitting lme model for feature 50 [longCombat]
fitting lme model for feature 51 [longCombat] fitting lme model for
feature 52 [longCombat] fitting lme model for feature 53 [longCombat]
fitting lme model for feature 54 [longCombat] fitting lme model for
feature 55 [longCombat] fitting lme model for feature 56 [longCombat]
fitting lme model for feature 57 [longCombat] fitting lme model for
feature 58 [longCombat] fitting lme model for feature 59 [longCombat]
fitting lme model for feature 60 [longCombat] fitting lme model for
feature 61 [longCombat] fitting lme model for feature 62 [longCombat]
fitting lme model for feature 63 [longCombat] fitting lme model for
feature 64 [longCombat] fitting lme model for feature 65 [longCombat]
fitting lme model for feature 66 [longCombat] fitting lme model for
feature 67 [longCombat] fitting lme model for feature 68 [longCombat]
fitting lme model for feature 69 [longCombat] fitting lme model for
feature 70 [longCombat] fitting lme model for feature 71 [longCombat]
using method of moments to estimate hyperparameters [longCombat] using
empirical Bayes to estimate batch effects… [longCombat] initializing…
[longCombat] starting EM algorithm iteration 1 [longCombat] starting EM
algorithm iteration 2 [longCombat] starting EM algorithm iteration 3
[longCombat] starting EM algorithm iteration 4 [longCombat] starting EM
algorithm iteration 5 [longCombat] starting EM algorithm iteration 6
[longCombat] starting EM algorithm iteration 7 [longCombat] starting EM
algorithm iteration 8 [longCombat] starting EM algorithm iteration 9
[longCombat] starting EM algorithm iteration 10 [longCombat] starting EM
algorithm iteration 11 [longCombat] starting EM algorithm iteration 12
[longCombat] starting EM algorithm iteration 13 [longCombat] starting EM
algorithm iteration 14 [longCombat] starting EM algorithm iteration 15
[longCombat] starting EM algorithm iteration 16 [longCombat] starting EM
algorithm iteration 17 [longCombat] starting EM algorithm iteration 18
[longCombat] starting EM algorithm iteration 19 [longCombat] starting EM
algorithm iteration 20 [longCombat] starting EM algorithm iteration 21
[longCombat] starting EM algorithm iteration 22 [longCombat] starting EM
algorithm iteration 23 [longCombat] starting EM algorithm iteration 24
[longCombat] starting EM algorithm iteration 25 [longCombat] starting EM
algorithm iteration 26 [longCombat] starting EM algorithm iteration 27
[longCombat] starting EM algorithm iteration 28 [longCombat] starting EM
algorithm iteration 29 [longCombat] starting EM algorithm iteration 30
[longCombat] adjusting data for batch effects
Harmonization Model: age
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6
dataset: thickness | sex: m |
region: smri_thick_cdk_cdmdfrlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-12255.3
|
-12227.97
|
6131.651
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-12363.7
|
-12309.03
|
6189.848
|
1 vs 2
|
116.3952
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-10226.99
|
-10199.65
|
5117.495
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-10219.05
|
-10164.38
|
5117.524
|
1 vs 2
|
0.0585278
|
0.9995801
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
2.779
|
0.132
|
|
brain_metric
|
scanner2
|
1808
|
2.829
|
0.144
|
|
brain_metric
|
scanner3
|
196
|
2.756
|
0.129
|
|
brain_metric
|
scanner5
|
2016
|
2.841
|
0.111
|
|
brain_metric
|
scanner6
|
2264
|
2.848
|
0.121
|
|
brain_metric.combat
|
scanner1
|
579
|
2.833
|
0.138
|
|
brain_metric.combat
|
scanner2
|
1808
|
2.835
|
0.141
|
|
brain_metric.combat
|
scanner3
|
196
|
2.819
|
0.133
|
|
brain_metric.combat
|
scanner5
|
2016
|
2.828
|
0.131
|
|
brain_metric.combat
|
scanner6
|
2264
|
2.834
|
0.137
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-0.014
|
|
|
|
|
age_slope
|
|
-0.014
|
|
|
|
scanner:scanner3
|
2.769
|
2.829
|
0.060
|
2.163
|
|
scanner:scanner1
|
2.777
|
2.832
|
0.055
|
1.975
|
|
scanner:scanner6
|
2.844
|
2.831
|
-0.014
|
-0.477
|
|
scanner:scanner5
|
2.844
|
2.831
|
-0.012
|
-0.432
|
|
scanner:scanner2
|
2.825
|
2.831
|
0.007
|
0.237
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_timing
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled
dataset: thickness | sex: m |
region: smri_thick_cdk_cdmdfrlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-12255.3
|
-12227.97
|
6131.651
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-12363.7
|
-12309.03
|
6189.848
|
1 vs 2
|
116.3952
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-10228.03
|
-10200.69
|
5118.014
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-10220.18
|
-10165.51
|
5118.090
|
1 vs 2
|
0.1534112
|
0.9972043
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
2.779
|
0.132
|
|
brain_metric
|
scanner2
|
1808
|
2.829
|
0.144
|
|
brain_metric
|
scanner3
|
196
|
2.756
|
0.129
|
|
brain_metric
|
scanner5
|
2016
|
2.841
|
0.111
|
|
brain_metric
|
scanner6
|
2264
|
2.848
|
0.121
|
|
brain_metric.combat
|
scanner1
|
579
|
2.834
|
0.138
|
|
brain_metric.combat
|
scanner2
|
1808
|
2.835
|
0.141
|
|
brain_metric.combat
|
scanner3
|
196
|
2.817
|
0.133
|
|
brain_metric.combat
|
scanner5
|
2016
|
2.828
|
0.131
|
|
brain_metric.combat
|
scanner6
|
2264
|
2.834
|
0.137
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-0.014
|
|
|
|
|
age_slope
|
|
-0.014
|
|
|
|
scanner:scanner3
|
2.769
|
2.827
|
0.058
|
2.110
|
|
scanner:scanner1
|
2.777
|
2.832
|
0.056
|
2.001
|
|
scanner:scanner6
|
2.844
|
2.831
|
-0.014
|
-0.480
|
|
scanner:scanner5
|
2.844
|
2.831
|
-0.012
|
-0.427
|
|
scanner:scanner2
|
2.825
|
2.831
|
0.007
|
0.234
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_tempo
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled
dataset: thickness | sex: m |
region: smri_thick_cdk_cdmdfrlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-12255.3
|
-12227.97
|
6131.651
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-12363.7
|
-12309.03
|
6189.848
|
1 vs 2
|
116.3952
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-10226.93
|
-10199.59
|
5117.465
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-10219.00
|
-10164.32
|
5117.498
|
1 vs 2
|
0.0666898
|
0.9994563
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
2.779
|
0.132
|
|
brain_metric
|
scanner2
|
1808
|
2.829
|
0.144
|
|
brain_metric
|
scanner3
|
196
|
2.756
|
0.129
|
|
brain_metric
|
scanner5
|
2016
|
2.841
|
0.111
|
|
brain_metric
|
scanner6
|
2264
|
2.848
|
0.121
|
|
brain_metric.combat
|
scanner1
|
579
|
2.833
|
0.138
|
|
brain_metric.combat
|
scanner2
|
1808
|
2.835
|
0.141
|
|
brain_metric.combat
|
scanner3
|
196
|
2.819
|
0.133
|
|
brain_metric.combat
|
scanner5
|
2016
|
2.828
|
0.131
|
|
brain_metric.combat
|
scanner6
|
2264
|
2.834
|
0.137
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-0.014
|
|
|
|
|
age_slope
|
|
-0.014
|
|
|
|
scanner:scanner3
|
2.769
|
2.829
|
0.060
|
2.161
|
|
scanner:scanner1
|
2.777
|
2.832
|
0.055
|
1.977
|
|
scanner:scanner6
|
2.844
|
2.831
|
-0.014
|
-0.479
|
|
scanner:scanner5
|
2.844
|
2.831
|
-0.012
|
-0.427
|
|
scanner:scanner2
|
2.825
|
2.831
|
0.007
|
0.234
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_timing_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 +
s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 +
s_age_by_timing_6 + s_age_by_timing_7
dataset: thickness | sex: m |
region: smri_thick_cdk_cdmdfrlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-12255.3
|
-12227.97
|
6131.651
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-12363.7
|
-12309.03
|
6189.848
|
1 vs 2
|
116.3952
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-10228.63
|
-10201.29
|
5118.314
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-10220.79
|
-10166.12
|
5118.396
|
1 vs 2
|
0.163817
|
0.9968232
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
2.779
|
0.132
|
|
brain_metric
|
scanner2
|
1808
|
2.829
|
0.144
|
|
brain_metric
|
scanner3
|
196
|
2.756
|
0.129
|
|
brain_metric
|
scanner5
|
2016
|
2.841
|
0.111
|
|
brain_metric
|
scanner6
|
2264
|
2.848
|
0.121
|
|
brain_metric.combat
|
scanner1
|
579
|
2.834
|
0.138
|
|
brain_metric.combat
|
scanner2
|
1808
|
2.835
|
0.141
|
|
brain_metric.combat
|
scanner3
|
196
|
2.817
|
0.133
|
|
brain_metric.combat
|
scanner5
|
2016
|
2.828
|
0.131
|
|
brain_metric.combat
|
scanner6
|
2264
|
2.835
|
0.137
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-0.014
|
|
|
|
|
age_slope
|
|
-0.014
|
|
|
|
scanner:scanner3
|
2.769
|
2.827
|
0.058
|
2.097
|
|
scanner:scanner1
|
2.777
|
2.833
|
0.056
|
2.003
|
|
scanner:scanner6
|
2.844
|
2.831
|
-0.013
|
-0.472
|
|
scanner:scanner5
|
2.844
|
2.831
|
-0.012
|
-0.437
|
|
scanner:scanner2
|
2.825
|
2.831
|
0.007
|
0.236
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_tempo_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 +
s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 +
s_age_by_tempo_6 + s_age_by_tempo_7
dataset: thickness | sex: m |
region: smri_thick_cdk_cdmdfrlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-12255.3
|
-12227.97
|
6131.651
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-12363.7
|
-12309.03
|
6189.848
|
1 vs 2
|
116.3952
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
-10224.97
|
-10197.63
|
5116.483
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
-10217.03
|
-10162.36
|
5116.513
|
1 vs 2
|
0.0608185
|
0.9995469
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
2.779
|
0.132
|
|
brain_metric
|
scanner2
|
1808
|
2.829
|
0.144
|
|
brain_metric
|
scanner3
|
196
|
2.756
|
0.129
|
|
brain_metric
|
scanner5
|
2016
|
2.841
|
0.111
|
|
brain_metric
|
scanner6
|
2264
|
2.848
|
0.121
|
|
brain_metric.combat
|
scanner1
|
579
|
2.833
|
0.138
|
|
brain_metric.combat
|
scanner2
|
1808
|
2.835
|
0.141
|
|
brain_metric.combat
|
scanner3
|
196
|
2.819
|
0.133
|
|
brain_metric.combat
|
scanner5
|
2016
|
2.828
|
0.131
|
|
brain_metric.combat
|
scanner6
|
2264
|
2.834
|
0.137
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-0.014
|
|
|
|
|
age_slope
|
|
-0.014
|
|
|
|
scanner:scanner3
|
2.769
|
2.829
|
0.060
|
2.166
|
|
scanner:scanner1
|
2.777
|
2.832
|
0.055
|
1.979
|
|
scanner:scanner6
|
2.844
|
2.831
|
-0.014
|
-0.478
|
|
scanner:scanner5
|
2.844
|
2.831
|
-0.012
|
-0.428
|
|
scanner:scanner2
|
2.825
|
2.831
|
0.007
|
0.234
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
area — f
[longCombat] found 5 batches [longCombat] found 71 features
[longCombat] found 6004 total observations [longCombat] standardizing
data across features… [longCombat] fitting lme model for feature 1
[longCombat] fitting lme model for feature 2 [longCombat] fitting lme
model for feature 3 [longCombat] fitting lme model for feature 4
[longCombat] fitting lme model for feature 5 [longCombat] fitting lme
model for feature 6 [longCombat] fitting lme model for feature 7
[longCombat] fitting lme model for feature 8 [longCombat] fitting lme
model for feature 9 [longCombat] fitting lme model for feature 10
[longCombat] fitting lme model for feature 11 [longCombat] fitting lme
model for feature 12 [longCombat] fitting lme model for feature 13
[longCombat] fitting lme model for feature 14 [longCombat] fitting lme
model for feature 15 [longCombat] fitting lme model for feature 16
[longCombat] fitting lme model for feature 17 [longCombat] fitting lme
model for feature 18 [longCombat] fitting lme model for feature 19
[longCombat] fitting lme model for feature 20 [longCombat] fitting lme
model for feature 21 [longCombat] fitting lme model for feature 22
[longCombat] fitting lme model for feature 23 [longCombat] fitting lme
model for feature 24 [longCombat] fitting lme model for feature 25
[longCombat] fitting lme model for feature 26 [longCombat] fitting lme
model for feature 27 [longCombat] fitting lme model for feature 28
[longCombat] fitting lme model for feature 29 [longCombat] fitting lme
model for feature 30 [longCombat] fitting lme model for feature 31
[longCombat] fitting lme model for feature 32 [longCombat] fitting lme
model for feature 33 [longCombat] fitting lme model for feature 34
[longCombat] fitting lme model for feature 35 [longCombat] fitting lme
model for feature 36 [longCombat] fitting lme model for feature 37
[longCombat] fitting lme model for feature 38 [longCombat] fitting lme
model for feature 39 [longCombat] fitting lme model for feature 40
[longCombat] fitting lme model for feature 41 [longCombat] fitting lme
model for feature 42 [longCombat] fitting lme model for feature 43
[longCombat] fitting lme model for feature 44 [longCombat] fitting lme
model for feature 45 [longCombat] fitting lme model for feature 46
[longCombat] fitting lme model for feature 47 [longCombat] fitting lme
model for feature 48 [longCombat] fitting lme model for feature 49
[longCombat] fitting lme model for feature 50 [longCombat] fitting lme
model for feature 51 [longCombat] fitting lme model for feature 52
[longCombat] fitting lme model for feature 53 [longCombat] fitting lme
model for feature 54 [longCombat] fitting lme model for feature 55
[longCombat] fitting lme model for feature 56 [longCombat] fitting lme
model for feature 57 [longCombat] fitting lme model for feature 58
[longCombat] fitting lme model for feature 59 [longCombat] fitting lme
model for feature 60 [longCombat] fitting lme model for feature 61
[longCombat] fitting lme model for feature 62 [longCombat] fitting lme
model for feature 63 [longCombat] fitting lme model for feature 64
[longCombat] fitting lme model for feature 65 [longCombat] fitting lme
model for feature 66 [longCombat] fitting lme model for feature 67
[longCombat] fitting lme model for feature 68 [longCombat] fitting lme
model for feature 69 [longCombat] fitting lme model for feature 70
[longCombat] fitting lme model for feature 71 [longCombat] using method
of moments to estimate hyperparameters [longCombat] using empirical
Bayes to estimate batch effects… [longCombat] initializing… [longCombat]
starting EM algorithm iteration 1 [longCombat] starting EM algorithm
iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat]
starting EM algorithm iteration 4 [longCombat] starting EM algorithm
iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat]
starting EM algorithm iteration 7 [longCombat] starting EM algorithm
iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat]
starting EM algorithm iteration 10 [longCombat] starting EM algorithm
iteration 11 [longCombat] starting EM algorithm iteration 12
[longCombat] starting EM algorithm iteration 13 [longCombat] starting EM
algorithm iteration 14 [longCombat] starting EM algorithm iteration 15
[longCombat] starting EM algorithm iteration 16 [longCombat] starting EM
algorithm iteration 17 [longCombat] starting EM algorithm iteration 18
[longCombat] starting EM algorithm iteration 19 [longCombat] starting EM
algorithm iteration 20 [longCombat] starting EM algorithm iteration 21
[longCombat] starting EM algorithm iteration 22 [longCombat] starting EM
algorithm iteration 23 [longCombat] starting EM algorithm iteration 24
[longCombat] starting EM algorithm iteration 25 [longCombat] starting EM
algorithm iteration 26 [longCombat] starting EM algorithm iteration 27
[longCombat] starting EM algorithm iteration 28 [longCombat] starting EM
algorithm iteration 29 [longCombat] starting EM algorithm iteration 30
[longCombat] adjusting data for batch effects [longCombat] found 5
batches [longCombat] found 71 features [longCombat] found 6004 total
observations [longCombat] standardizing data across features…
[longCombat] fitting lme model for feature 1 [longCombat] fitting lme
model for feature 2 [longCombat] fitting lme model for feature 3
[longCombat] fitting lme model for feature 4 [longCombat] fitting lme
model for feature 5 [longCombat] fitting lme model for feature 6
[longCombat] fitting lme model for feature 7 [longCombat] fitting lme
model for feature 8 [longCombat] fitting lme model for feature 9
[longCombat] fitting lme model for feature 10 [longCombat] fitting lme
model for feature 11 [longCombat] fitting lme model for feature 12
[longCombat] fitting lme model for feature 13 [longCombat] fitting lme
model for feature 14 [longCombat] fitting lme model for feature 15
[longCombat] fitting lme model for feature 16 [longCombat] fitting lme
model for feature 17 [longCombat] fitting lme model for feature 18
[longCombat] fitting lme model for feature 19 [longCombat] fitting lme
model for feature 20 [longCombat] fitting lme model for feature 21
[longCombat] fitting lme model for feature 22 [longCombat] fitting lme
model for feature 23 [longCombat] fitting lme model for feature 24
[longCombat] fitting lme model for feature 25 [longCombat] fitting lme
model for feature 26 [longCombat] fitting lme model for feature 27
[longCombat] fitting lme model for feature 28 [longCombat] fitting lme
model for feature 29 [longCombat] fitting lme model for feature 30
[longCombat] fitting lme model for feature 31 [longCombat] fitting lme
model for feature 32 [longCombat] fitting lme model for feature 33
[longCombat] fitting lme model for feature 34 [longCombat] fitting lme
model for feature 35 [longCombat] fitting lme model for feature 36
[longCombat] fitting lme model for feature 37 [longCombat] fitting lme
model for feature 38 [longCombat] fitting lme model for feature 39
[longCombat] fitting lme model for feature 40 [longCombat] fitting lme
model for feature 41 [longCombat] fitting lme model for feature 42
[longCombat] fitting lme model for feature 43 [longCombat] fitting lme
model for feature 44 [longCombat] fitting lme model for feature 45
[longCombat] fitting lme model for feature 46 [longCombat] fitting lme
model for feature 47 [longCombat] fitting lme model for feature 48
[longCombat] fitting lme model for feature 49 [longCombat] fitting lme
model for feature 50 [longCombat] fitting lme model for feature 51
[longCombat] fitting lme model for feature 52 [longCombat] fitting lme
model for feature 53 [longCombat] fitting lme model for feature 54
[longCombat] fitting lme model for feature 55 [longCombat] fitting lme
model for feature 56 [longCombat] fitting lme model for feature 57
[longCombat] fitting lme model for feature 58 [longCombat] fitting lme
model for feature 59 [longCombat] fitting lme model for feature 60
[longCombat] fitting lme model for feature 61 [longCombat] fitting lme
model for feature 62 [longCombat] fitting lme model for feature 63
[longCombat] fitting lme model for feature 64 [longCombat] fitting lme
model for feature 65 [longCombat] fitting lme model for feature 66
[longCombat] fitting lme model for feature 67 [longCombat] fitting lme
model for feature 68 [longCombat] fitting lme model for feature 69
[longCombat] fitting lme model for feature 70 [longCombat] fitting lme
model for feature 71 [longCombat] using method of moments to estimate
hyperparameters [longCombat] using empirical Bayes to estimate batch
effects… [longCombat] initializing… [longCombat] starting EM algorithm
iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat]
starting EM algorithm iteration 3 [longCombat] starting EM algorithm
iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat]
starting EM algorithm iteration 6 [longCombat] starting EM algorithm
iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat]
starting EM algorithm iteration 9 [longCombat] starting EM algorithm
iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
71 features [longCombat] found 6004 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] fitting lme model for
feature 43 [longCombat] fitting lme model for feature 44 [longCombat]
fitting lme model for feature 45 [longCombat] fitting lme model for
feature 46 [longCombat] fitting lme model for feature 47 [longCombat]
fitting lme model for feature 48 [longCombat] fitting lme model for
feature 49 [longCombat] fitting lme model for feature 50 [longCombat]
fitting lme model for feature 51 [longCombat] fitting lme model for
feature 52 [longCombat] fitting lme model for feature 53 [longCombat]
fitting lme model for feature 54 [longCombat] fitting lme model for
feature 55 [longCombat] fitting lme model for feature 56 [longCombat]
fitting lme model for feature 57 [longCombat] fitting lme model for
feature 58 [longCombat] fitting lme model for feature 59 [longCombat]
fitting lme model for feature 60 [longCombat] fitting lme model for
feature 61 [longCombat] fitting lme model for feature 62 [longCombat]
fitting lme model for feature 63 [longCombat] fitting lme model for
feature 64 [longCombat] fitting lme model for feature 65 [longCombat]
fitting lme model for feature 66 [longCombat] fitting lme model for
feature 67 [longCombat] fitting lme model for feature 68 [longCombat]
fitting lme model for feature 69 [longCombat] fitting lme model for
feature 70 [longCombat] fitting lme model for feature 71 [longCombat]
using method of moments to estimate hyperparameters [longCombat] using
empirical Bayes to estimate batch effects… [longCombat] initializing…
[longCombat] starting EM algorithm iteration 1 [longCombat] starting EM
algorithm iteration 2 [longCombat] starting EM algorithm iteration 3
[longCombat] starting EM algorithm iteration 4 [longCombat] starting EM
algorithm iteration 5 [longCombat] starting EM algorithm iteration 6
[longCombat] starting EM algorithm iteration 7 [longCombat] starting EM
algorithm iteration 8 [longCombat] starting EM algorithm iteration 9
[longCombat] starting EM algorithm iteration 10 [longCombat] starting EM
algorithm iteration 11 [longCombat] starting EM algorithm iteration 12
[longCombat] starting EM algorithm iteration 13 [longCombat] starting EM
algorithm iteration 14 [longCombat] starting EM algorithm iteration 15
[longCombat] starting EM algorithm iteration 16 [longCombat] starting EM
algorithm iteration 17 [longCombat] starting EM algorithm iteration 18
[longCombat] starting EM algorithm iteration 19 [longCombat] starting EM
algorithm iteration 20 [longCombat] starting EM algorithm iteration 21
[longCombat] starting EM algorithm iteration 22 [longCombat] starting EM
algorithm iteration 23 [longCombat] starting EM algorithm iteration 24
[longCombat] starting EM algorithm iteration 25 [longCombat] starting EM
algorithm iteration 26 [longCombat] starting EM algorithm iteration 27
[longCombat] starting EM algorithm iteration 28 [longCombat] starting EM
algorithm iteration 29 [longCombat] starting EM algorithm iteration 30
[longCombat] adjusting data for batch effects [longCombat] found 5
batches [longCombat] found 71 features [longCombat] found 6004 total
observations [longCombat] standardizing data across features…
[longCombat] fitting lme model for feature 1 [longCombat] fitting lme
model for feature 2 [longCombat] fitting lme model for feature 3
[longCombat] fitting lme model for feature 4 [longCombat] fitting lme
model for feature 5 [longCombat] fitting lme model for feature 6
[longCombat] fitting lme model for feature 7 [longCombat] fitting lme
model for feature 8 [longCombat] fitting lme model for feature 9
[longCombat] fitting lme model for feature 10 [longCombat] fitting lme
model for feature 11 [longCombat] fitting lme model for feature 12
[longCombat] fitting lme model for feature 13 [longCombat] fitting lme
model for feature 14 [longCombat] fitting lme model for feature 15
[longCombat] fitting lme model for feature 16 [longCombat] fitting lme
model for feature 17 [longCombat] fitting lme model for feature 18
[longCombat] fitting lme model for feature 19 [longCombat] fitting lme
model for feature 20 [longCombat] fitting lme model for feature 21
[longCombat] fitting lme model for feature 22 [longCombat] fitting lme
model for feature 23 [longCombat] fitting lme model for feature 24
[longCombat] fitting lme model for feature 25 [longCombat] fitting lme
model for feature 26 [longCombat] fitting lme model for feature 27
[longCombat] fitting lme model for feature 28 [longCombat] fitting lme
model for feature 29 [longCombat] fitting lme model for feature 30
[longCombat] fitting lme model for feature 31 [longCombat] fitting lme
model for feature 32 [longCombat] fitting lme model for feature 33
[longCombat] fitting lme model for feature 34 [longCombat] fitting lme
model for feature 35 [longCombat] fitting lme model for feature 36
[longCombat] fitting lme model for feature 37 [longCombat] fitting lme
model for feature 38 [longCombat] fitting lme model for feature 39
[longCombat] fitting lme model for feature 40 [longCombat] fitting lme
model for feature 41 [longCombat] fitting lme model for feature 42
[longCombat] fitting lme model for feature 43 [longCombat] fitting lme
model for feature 44 [longCombat] fitting lme model for feature 45
[longCombat] fitting lme model for feature 46 [longCombat] fitting lme
model for feature 47 [longCombat] fitting lme model for feature 48
[longCombat] fitting lme model for feature 49 [longCombat] fitting lme
model for feature 50 [longCombat] fitting lme model for feature 51
[longCombat] fitting lme model for feature 52 [longCombat] fitting lme
model for feature 53 [longCombat] fitting lme model for feature 54
[longCombat] fitting lme model for feature 55 [longCombat] fitting lme
model for feature 56 [longCombat] fitting lme model for feature 57
[longCombat] fitting lme model for feature 58 [longCombat] fitting lme
model for feature 59 [longCombat] fitting lme model for feature 60
[longCombat] fitting lme model for feature 61 [longCombat] fitting lme
model for feature 62 [longCombat] fitting lme model for feature 63
[longCombat] fitting lme model for feature 64 [longCombat] fitting lme
model for feature 65 [longCombat] fitting lme model for feature 66
[longCombat] fitting lme model for feature 67 [longCombat] fitting lme
model for feature 68 [longCombat] fitting lme model for feature 69
[longCombat] fitting lme model for feature 70 [longCombat] fitting lme
model for feature 71 [longCombat] using method of moments to estimate
hyperparameters [longCombat] using empirical Bayes to estimate batch
effects… [longCombat] initializing… [longCombat] starting EM algorithm
iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat]
starting EM algorithm iteration 3 [longCombat] starting EM algorithm
iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat]
starting EM algorithm iteration 6 [longCombat] starting EM algorithm
iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat]
starting EM algorithm iteration 9 [longCombat] starting EM algorithm
iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
71 features [longCombat] found 6004 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] fitting lme model for
feature 43 [longCombat] fitting lme model for feature 44 [longCombat]
fitting lme model for feature 45 [longCombat] fitting lme model for
feature 46 [longCombat] fitting lme model for feature 47 [longCombat]
fitting lme model for feature 48 [longCombat] fitting lme model for
feature 49 [longCombat] fitting lme model for feature 50 [longCombat]
fitting lme model for feature 51 [longCombat] fitting lme model for
feature 52 [longCombat] fitting lme model for feature 53 [longCombat]
fitting lme model for feature 54 [longCombat] fitting lme model for
feature 55 [longCombat] fitting lme model for feature 56 [longCombat]
fitting lme model for feature 57 [longCombat] fitting lme model for
feature 58 [longCombat] fitting lme model for feature 59 [longCombat]
fitting lme model for feature 60 [longCombat] fitting lme model for
feature 61 [longCombat] fitting lme model for feature 62 [longCombat]
fitting lme model for feature 63 [longCombat] fitting lme model for
feature 64 [longCombat] fitting lme model for feature 65 [longCombat]
fitting lme model for feature 66 [longCombat] fitting lme model for
feature 67 [longCombat] fitting lme model for feature 68 [longCombat]
fitting lme model for feature 69 [longCombat] fitting lme model for
feature 70 [longCombat] fitting lme model for feature 71 [longCombat]
using method of moments to estimate hyperparameters [longCombat] using
empirical Bayes to estimate batch effects… [longCombat] initializing…
[longCombat] starting EM algorithm iteration 1 [longCombat] starting EM
algorithm iteration 2 [longCombat] starting EM algorithm iteration 3
[longCombat] starting EM algorithm iteration 4 [longCombat] starting EM
algorithm iteration 5 [longCombat] starting EM algorithm iteration 6
[longCombat] starting EM algorithm iteration 7 [longCombat] starting EM
algorithm iteration 8 [longCombat] starting EM algorithm iteration 9
[longCombat] starting EM algorithm iteration 10 [longCombat] starting EM
algorithm iteration 11 [longCombat] starting EM algorithm iteration 12
[longCombat] starting EM algorithm iteration 13 [longCombat] starting EM
algorithm iteration 14 [longCombat] starting EM algorithm iteration 15
[longCombat] starting EM algorithm iteration 16 [longCombat] starting EM
algorithm iteration 17 [longCombat] starting EM algorithm iteration 18
[longCombat] starting EM algorithm iteration 19 [longCombat] starting EM
algorithm iteration 20 [longCombat] starting EM algorithm iteration 21
[longCombat] starting EM algorithm iteration 22 [longCombat] starting EM
algorithm iteration 23 [longCombat] starting EM algorithm iteration 24
[longCombat] starting EM algorithm iteration 25 [longCombat] starting EM
algorithm iteration 26 [longCombat] starting EM algorithm iteration 27
[longCombat] starting EM algorithm iteration 28 [longCombat] starting EM
algorithm iteration 29 [longCombat] starting EM algorithm iteration 30
[longCombat] adjusting data for batch effects
Harmonization Model: age
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6
dataset: area | sex: f |
region: smri_area_cdk_mdtmlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
82923.34
|
82950.14
|
-41457.67
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
82778.44
|
82832.04
|
-41381.22
|
1 vs 2
|
152.9054
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
84702.09
|
84728.89
|
-42347.05
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
84709.40
|
84763.00
|
-42346.70
|
1 vs 2
|
0.6961797
|
0.9517992
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
3449.066
|
402.696
|
|
brain_metric
|
scanner2
|
1720
|
3640.774
|
451.854
|
|
brain_metric
|
scanner3
|
204
|
3349.922
|
398.007
|
|
brain_metric
|
scanner5
|
1596
|
3478.284
|
413.083
|
|
brain_metric
|
scanner6
|
2015
|
3463.817
|
412.676
|
|
brain_metric.combat
|
scanner1
|
469
|
3542.788
|
422.232
|
|
brain_metric.combat
|
scanner2
|
1720
|
3509.192
|
448.405
|
|
brain_metric.combat
|
scanner3
|
204
|
3508.632
|
418.113
|
|
brain_metric.combat
|
scanner5
|
1596
|
3509.699
|
427.640
|
|
brain_metric.combat
|
scanner6
|
2015
|
3513.268
|
437.272
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-11.067
|
|
|
|
|
age_slope
|
|
-6.562
|
|
|
|
scanner:scanner3
|
3350.180
|
3511.397
|
161.217
|
4.812
|
|
scanner:scanner2
|
3640.186
|
3510.750
|
-129.436
|
-3.556
|
|
scanner:scanner1
|
3414.452
|
3504.021
|
89.568
|
2.623
|
|
scanner:scanner6
|
3459.880
|
3512.040
|
52.160
|
1.508
|
|
scanner:scanner5
|
3473.810
|
3502.206
|
28.396
|
0.817
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_timing
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled
dataset: area | sex: f |
region: smri_area_cdk_mdtmlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
82923.34
|
82950.14
|
-41457.67
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
82778.44
|
82832.04
|
-41381.22
|
1 vs 2
|
152.9054
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
84700.83
|
84727.64
|
-42346.42
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
84708.30
|
84761.90
|
-42346.15
|
1 vs 2
|
0.5327495
|
0.9702355
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
3449.066
|
402.696
|
|
brain_metric
|
scanner2
|
1720
|
3640.774
|
451.854
|
|
brain_metric
|
scanner3
|
204
|
3349.922
|
398.007
|
|
brain_metric
|
scanner5
|
1596
|
3478.284
|
413.083
|
|
brain_metric
|
scanner6
|
2015
|
3463.817
|
412.676
|
|
brain_metric.combat
|
scanner1
|
469
|
3545.002
|
422.183
|
|
brain_metric.combat
|
scanner2
|
1720
|
3508.670
|
448.402
|
|
brain_metric.combat
|
scanner3
|
204
|
3504.084
|
417.978
|
|
brain_metric.combat
|
scanner5
|
1596
|
3510.262
|
427.653
|
|
brain_metric.combat
|
scanner6
|
2015
|
3513.212
|
437.232
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-11.067
|
|
|
|
|
age_slope
|
|
-6.506
|
|
|
|
scanner:scanner3
|
3350.180
|
3506.499
|
156.319
|
4.666
|
|
scanner:scanner2
|
3640.186
|
3510.207
|
-129.978
|
-3.571
|
|
scanner:scanner1
|
3414.452
|
3506.530
|
92.078
|
2.697
|
|
scanner:scanner6
|
3459.880
|
3511.809
|
51.929
|
1.501
|
|
scanner:scanner5
|
3473.810
|
3502.994
|
29.184
|
0.840
|
|
scanner:scanner7
|
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|
|
|
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scanner:scanner8
|
|
|
|
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Harmonization Model: age_tempo
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled
dataset: area | sex: f |
region: smri_area_cdk_mdtmlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
82923.34
|
82950.14
|
-41457.67
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
82778.44
|
82832.04
|
-41381.22
|
1 vs 2
|
152.9054
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
84702.05
|
84728.85
|
-42347.02
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
84709.37
|
84762.97
|
-42346.69
|
1 vs 2
|
0.6736663
|
0.9545362
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
3449.066
|
402.696
|
|
brain_metric
|
scanner2
|
1720
|
3640.774
|
451.854
|
|
brain_metric
|
scanner3
|
204
|
3349.922
|
398.007
|
|
brain_metric
|
scanner5
|
1596
|
3478.284
|
413.083
|
|
brain_metric
|
scanner6
|
2015
|
3463.817
|
412.676
|
|
brain_metric.combat
|
scanner1
|
469
|
3543.958
|
422.249
|
|
brain_metric.combat
|
scanner2
|
1720
|
3508.953
|
448.405
|
|
brain_metric.combat
|
scanner3
|
204
|
3506.901
|
418.066
|
|
brain_metric.combat
|
scanner5
|
1596
|
3509.757
|
427.639
|
|
brain_metric.combat
|
scanner6
|
2015
|
3513.328
|
437.265
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-11.067
|
|
|
|
|
age_slope
|
|
-6.553
|
|
|
|
scanner:scanner3
|
3350.180
|
3509.531
|
159.351
|
4.756
|
|
scanner:scanner2
|
3640.186
|
3510.494
|
-129.692
|
-3.563
|
|
scanner:scanner1
|
3414.452
|
3505.359
|
90.907
|
2.662
|
|
scanner:scanner6
|
3459.880
|
3512.099
|
52.219
|
1.509
|
|
scanner:scanner5
|
3473.810
|
3502.241
|
28.431
|
0.818
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_timing_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 +
s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 +
s_age_by_timing_6 + s_age_by_timing_7
dataset: area | sex: f |
region: smri_area_cdk_mdtmlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
82923.34
|
82950.14
|
-41457.67
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
82778.44
|
82832.04
|
-41381.22
|
1 vs 2
|
152.9054
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
84690.86
|
84717.66
|
-42341.43
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
84698.18
|
84751.78
|
-42341.09
|
1 vs 2
|
0.6780178
|
0.9540118
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
3449.066
|
402.696
|
|
brain_metric
|
scanner2
|
1720
|
3640.774
|
451.854
|
|
brain_metric
|
scanner3
|
204
|
3349.922
|
398.007
|
|
brain_metric
|
scanner5
|
1596
|
3478.284
|
413.083
|
|
brain_metric
|
scanner6
|
2015
|
3463.817
|
412.676
|
|
brain_metric.combat
|
scanner1
|
469
|
3544.602
|
422.142
|
|
brain_metric.combat
|
scanner2
|
1720
|
3508.565
|
448.352
|
|
brain_metric.combat
|
scanner3
|
204
|
3502.533
|
417.301
|
|
brain_metric.combat
|
scanner5
|
1596
|
3509.773
|
427.543
|
|
brain_metric.combat
|
scanner6
|
2015
|
3513.944
|
437.663
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-11.067
|
|
|
|
|
age_slope
|
|
-6.462
|
|
|
|
scanner:scanner3
|
3350.180
|
3505.712
|
155.532
|
4.642
|
|
scanner:scanner2
|
3640.186
|
3510.124
|
-130.061
|
-3.573
|
|
scanner:scanner1
|
3414.452
|
3505.815
|
91.363
|
2.676
|
|
scanner:scanner6
|
3459.880
|
3512.489
|
52.610
|
1.521
|
|
scanner:scanner5
|
3473.810
|
3502.544
|
28.734
|
0.827
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_tempo_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 +
s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 +
s_age_by_tempo_6 + s_age_by_tempo_7
dataset: area | sex: f |
region: smri_area_cdk_mdtmlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
82923.34
|
82950.14
|
-41457.67
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
82778.44
|
82832.04
|
-41381.22
|
1 vs 2
|
152.9054
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
84704.75
|
84731.55
|
-42348.37
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
84712.07
|
84765.67
|
-42348.03
|
1 vs 2
|
0.6797435
|
0.9538033
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
3449.066
|
402.696
|
|
brain_metric
|
scanner2
|
1720
|
3640.774
|
451.854
|
|
brain_metric
|
scanner3
|
204
|
3349.922
|
398.007
|
|
brain_metric
|
scanner5
|
1596
|
3478.284
|
413.083
|
|
brain_metric
|
scanner6
|
2015
|
3463.817
|
412.676
|
|
brain_metric.combat
|
scanner1
|
469
|
3544.577
|
422.383
|
|
brain_metric.combat
|
scanner2
|
1720
|
3509.098
|
448.447
|
|
brain_metric.combat
|
scanner3
|
204
|
3506.549
|
418.049
|
|
brain_metric.combat
|
scanner5
|
1596
|
3509.571
|
427.648
|
|
brain_metric.combat
|
scanner6
|
2015
|
3513.244
|
437.249
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
-11.067
|
|
|
|
|
age_slope
|
|
-6.544
|
|
|
|
scanner:scanner3
|
3350.180
|
3509.334
|
159.153
|
4.751
|
|
scanner:scanner2
|
3640.186
|
3510.580
|
-129.605
|
-3.560
|
|
scanner:scanner1
|
3414.452
|
3505.644
|
91.192
|
2.671
|
|
scanner:scanner6
|
3459.880
|
3512.063
|
52.183
|
1.508
|
|
scanner:scanner5
|
3473.810
|
3502.153
|
28.343
|
0.816
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
area — m
[longCombat] found 5 batches [longCombat] found 71 features
[longCombat] found 6863 total observations [longCombat] standardizing
data across features… [longCombat] fitting lme model for feature 1
[longCombat] fitting lme model for feature 2 [longCombat] fitting lme
model for feature 3 [longCombat] fitting lme model for feature 4
[longCombat] fitting lme model for feature 5 [longCombat] fitting lme
model for feature 6 [longCombat] fitting lme model for feature 7
[longCombat] fitting lme model for feature 8 [longCombat] fitting lme
model for feature 9 [longCombat] fitting lme model for feature 10
[longCombat] fitting lme model for feature 11 [longCombat] fitting lme
model for feature 12 [longCombat] fitting lme model for feature 13
[longCombat] fitting lme model for feature 14 [longCombat] fitting lme
model for feature 15 [longCombat] fitting lme model for feature 16
[longCombat] fitting lme model for feature 17 [longCombat] fitting lme
model for feature 18 [longCombat] fitting lme model for feature 19
[longCombat] fitting lme model for feature 20 [longCombat] fitting lme
model for feature 21 [longCombat] fitting lme model for feature 22
[longCombat] fitting lme model for feature 23 [longCombat] fitting lme
model for feature 24 [longCombat] fitting lme model for feature 25
[longCombat] fitting lme model for feature 26 [longCombat] fitting lme
model for feature 27 [longCombat] fitting lme model for feature 28
[longCombat] fitting lme model for feature 29 [longCombat] fitting lme
model for feature 30 [longCombat] fitting lme model for feature 31
[longCombat] fitting lme model for feature 32 [longCombat] fitting lme
model for feature 33 [longCombat] fitting lme model for feature 34
[longCombat] fitting lme model for feature 35 [longCombat] fitting lme
model for feature 36 [longCombat] fitting lme model for feature 37
[longCombat] fitting lme model for feature 38 [longCombat] fitting lme
model for feature 39 [longCombat] fitting lme model for feature 40
[longCombat] fitting lme model for feature 41 [longCombat] fitting lme
model for feature 42 [longCombat] fitting lme model for feature 43
[longCombat] fitting lme model for feature 44 [longCombat] fitting lme
model for feature 45 [longCombat] fitting lme model for feature 46
[longCombat] fitting lme model for feature 47 [longCombat] fitting lme
model for feature 48 [longCombat] fitting lme model for feature 49
[longCombat] fitting lme model for feature 50 [longCombat] fitting lme
model for feature 51 [longCombat] fitting lme model for feature 52
[longCombat] fitting lme model for feature 53 [longCombat] fitting lme
model for feature 54 [longCombat] fitting lme model for feature 55
[longCombat] fitting lme model for feature 56 [longCombat] fitting lme
model for feature 57 [longCombat] fitting lme model for feature 58
[longCombat] fitting lme model for feature 59 [longCombat] fitting lme
model for feature 60 [longCombat] fitting lme model for feature 61
[longCombat] fitting lme model for feature 62 [longCombat] fitting lme
model for feature 63 [longCombat] fitting lme model for feature 64
[longCombat] fitting lme model for feature 65 [longCombat] fitting lme
model for feature 66 [longCombat] fitting lme model for feature 67
[longCombat] fitting lme model for feature 68 [longCombat] fitting lme
model for feature 69 [longCombat] fitting lme model for feature 70
[longCombat] fitting lme model for feature 71 [longCombat] using method
of moments to estimate hyperparameters [longCombat] using empirical
Bayes to estimate batch effects… [longCombat] initializing… [longCombat]
starting EM algorithm iteration 1 [longCombat] starting EM algorithm
iteration 2 [longCombat] starting EM algorithm iteration 3 [longCombat]
starting EM algorithm iteration 4 [longCombat] starting EM algorithm
iteration 5 [longCombat] starting EM algorithm iteration 6 [longCombat]
starting EM algorithm iteration 7 [longCombat] starting EM algorithm
iteration 8 [longCombat] starting EM algorithm iteration 9 [longCombat]
starting EM algorithm iteration 10 [longCombat] starting EM algorithm
iteration 11 [longCombat] starting EM algorithm iteration 12
[longCombat] starting EM algorithm iteration 13 [longCombat] starting EM
algorithm iteration 14 [longCombat] starting EM algorithm iteration 15
[longCombat] starting EM algorithm iteration 16 [longCombat] starting EM
algorithm iteration 17 [longCombat] starting EM algorithm iteration 18
[longCombat] starting EM algorithm iteration 19 [longCombat] starting EM
algorithm iteration 20 [longCombat] starting EM algorithm iteration 21
[longCombat] starting EM algorithm iteration 22 [longCombat] starting EM
algorithm iteration 23 [longCombat] starting EM algorithm iteration 24
[longCombat] starting EM algorithm iteration 25 [longCombat] starting EM
algorithm iteration 26 [longCombat] starting EM algorithm iteration 27
[longCombat] starting EM algorithm iteration 28 [longCombat] starting EM
algorithm iteration 29 [longCombat] starting EM algorithm iteration 30
[longCombat] adjusting data for batch effects [longCombat] found 5
batches [longCombat] found 71 features [longCombat] found 6863 total
observations [longCombat] standardizing data across features…
[longCombat] fitting lme model for feature 1 [longCombat] fitting lme
model for feature 2 [longCombat] fitting lme model for feature 3
[longCombat] fitting lme model for feature 4 [longCombat] fitting lme
model for feature 5 [longCombat] fitting lme model for feature 6
[longCombat] fitting lme model for feature 7 [longCombat] fitting lme
model for feature 8 [longCombat] fitting lme model for feature 9
[longCombat] fitting lme model for feature 10 [longCombat] fitting lme
model for feature 11 [longCombat] fitting lme model for feature 12
[longCombat] fitting lme model for feature 13 [longCombat] fitting lme
model for feature 14 [longCombat] fitting lme model for feature 15
[longCombat] fitting lme model for feature 16 [longCombat] fitting lme
model for feature 17 [longCombat] fitting lme model for feature 18
[longCombat] fitting lme model for feature 19 [longCombat] fitting lme
model for feature 20 [longCombat] fitting lme model for feature 21
[longCombat] fitting lme model for feature 22 [longCombat] fitting lme
model for feature 23 [longCombat] fitting lme model for feature 24
[longCombat] fitting lme model for feature 25 [longCombat] fitting lme
model for feature 26 [longCombat] fitting lme model for feature 27
[longCombat] fitting lme model for feature 28 [longCombat] fitting lme
model for feature 29 [longCombat] fitting lme model for feature 30
[longCombat] fitting lme model for feature 31 [longCombat] fitting lme
model for feature 32 [longCombat] fitting lme model for feature 33
[longCombat] fitting lme model for feature 34 [longCombat] fitting lme
model for feature 35 [longCombat] fitting lme model for feature 36
[longCombat] fitting lme model for feature 37 [longCombat] fitting lme
model for feature 38 [longCombat] fitting lme model for feature 39
[longCombat] fitting lme model for feature 40 [longCombat] fitting lme
model for feature 41 [longCombat] fitting lme model for feature 42
[longCombat] fitting lme model for feature 43 [longCombat] fitting lme
model for feature 44 [longCombat] fitting lme model for feature 45
[longCombat] fitting lme model for feature 46 [longCombat] fitting lme
model for feature 47 [longCombat] fitting lme model for feature 48
[longCombat] fitting lme model for feature 49 [longCombat] fitting lme
model for feature 50 [longCombat] fitting lme model for feature 51
[longCombat] fitting lme model for feature 52 [longCombat] fitting lme
model for feature 53 [longCombat] fitting lme model for feature 54
[longCombat] fitting lme model for feature 55 [longCombat] fitting lme
model for feature 56 [longCombat] fitting lme model for feature 57
[longCombat] fitting lme model for feature 58 [longCombat] fitting lme
model for feature 59 [longCombat] fitting lme model for feature 60
[longCombat] fitting lme model for feature 61 [longCombat] fitting lme
model for feature 62 [longCombat] fitting lme model for feature 63
[longCombat] fitting lme model for feature 64 [longCombat] fitting lme
model for feature 65 [longCombat] fitting lme model for feature 66
[longCombat] fitting lme model for feature 67 [longCombat] fitting lme
model for feature 68 [longCombat] fitting lme model for feature 69
[longCombat] fitting lme model for feature 70 [longCombat] fitting lme
model for feature 71 [longCombat] using method of moments to estimate
hyperparameters [longCombat] using empirical Bayes to estimate batch
effects… [longCombat] initializing… [longCombat] starting EM algorithm
iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat]
starting EM algorithm iteration 3 [longCombat] starting EM algorithm
iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat]
starting EM algorithm iteration 6 [longCombat] starting EM algorithm
iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat]
starting EM algorithm iteration 9 [longCombat] starting EM algorithm
iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
71 features [longCombat] found 6863 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] fitting lme model for
feature 43 [longCombat] fitting lme model for feature 44 [longCombat]
fitting lme model for feature 45 [longCombat] fitting lme model for
feature 46 [longCombat] fitting lme model for feature 47 [longCombat]
fitting lme model for feature 48 [longCombat] fitting lme model for
feature 49 [longCombat] fitting lme model for feature 50 [longCombat]
fitting lme model for feature 51 [longCombat] fitting lme model for
feature 52 [longCombat] fitting lme model for feature 53 [longCombat]
fitting lme model for feature 54 [longCombat] fitting lme model for
feature 55 [longCombat] fitting lme model for feature 56 [longCombat]
fitting lme model for feature 57 [longCombat] fitting lme model for
feature 58 [longCombat] fitting lme model for feature 59 [longCombat]
fitting lme model for feature 60 [longCombat] fitting lme model for
feature 61 [longCombat] fitting lme model for feature 62 [longCombat]
fitting lme model for feature 63 [longCombat] fitting lme model for
feature 64 [longCombat] fitting lme model for feature 65 [longCombat]
fitting lme model for feature 66 [longCombat] fitting lme model for
feature 67 [longCombat] fitting lme model for feature 68 [longCombat]
fitting lme model for feature 69 [longCombat] fitting lme model for
feature 70 [longCombat] fitting lme model for feature 71 [longCombat]
using method of moments to estimate hyperparameters [longCombat] using
empirical Bayes to estimate batch effects… [longCombat] initializing…
[longCombat] starting EM algorithm iteration 1 [longCombat] starting EM
algorithm iteration 2 [longCombat] starting EM algorithm iteration 3
[longCombat] starting EM algorithm iteration 4 [longCombat] starting EM
algorithm iteration 5 [longCombat] starting EM algorithm iteration 6
[longCombat] starting EM algorithm iteration 7 [longCombat] starting EM
algorithm iteration 8 [longCombat] starting EM algorithm iteration 9
[longCombat] starting EM algorithm iteration 10 [longCombat] starting EM
algorithm iteration 11 [longCombat] starting EM algorithm iteration 12
[longCombat] starting EM algorithm iteration 13 [longCombat] starting EM
algorithm iteration 14 [longCombat] starting EM algorithm iteration 15
[longCombat] starting EM algorithm iteration 16 [longCombat] starting EM
algorithm iteration 17 [longCombat] starting EM algorithm iteration 18
[longCombat] starting EM algorithm iteration 19 [longCombat] starting EM
algorithm iteration 20 [longCombat] starting EM algorithm iteration 21
[longCombat] starting EM algorithm iteration 22 [longCombat] starting EM
algorithm iteration 23 [longCombat] starting EM algorithm iteration 24
[longCombat] starting EM algorithm iteration 25 [longCombat] starting EM
algorithm iteration 26 [longCombat] starting EM algorithm iteration 27
[longCombat] starting EM algorithm iteration 28 [longCombat] starting EM
algorithm iteration 29 [longCombat] starting EM algorithm iteration 30
[longCombat] adjusting data for batch effects [longCombat] found 5
batches [longCombat] found 71 features [longCombat] found 6863 total
observations [longCombat] standardizing data across features…
[longCombat] fitting lme model for feature 1 [longCombat] fitting lme
model for feature 2 [longCombat] fitting lme model for feature 3
[longCombat] fitting lme model for feature 4 [longCombat] fitting lme
model for feature 5 [longCombat] fitting lme model for feature 6
[longCombat] fitting lme model for feature 7 [longCombat] fitting lme
model for feature 8 [longCombat] fitting lme model for feature 9
[longCombat] fitting lme model for feature 10 [longCombat] fitting lme
model for feature 11 [longCombat] fitting lme model for feature 12
[longCombat] fitting lme model for feature 13 [longCombat] fitting lme
model for feature 14 [longCombat] fitting lme model for feature 15
[longCombat] fitting lme model for feature 16 [longCombat] fitting lme
model for feature 17 [longCombat] fitting lme model for feature 18
[longCombat] fitting lme model for feature 19 [longCombat] fitting lme
model for feature 20 [longCombat] fitting lme model for feature 21
[longCombat] fitting lme model for feature 22 [longCombat] fitting lme
model for feature 23 [longCombat] fitting lme model for feature 24
[longCombat] fitting lme model for feature 25 [longCombat] fitting lme
model for feature 26 [longCombat] fitting lme model for feature 27
[longCombat] fitting lme model for feature 28 [longCombat] fitting lme
model for feature 29 [longCombat] fitting lme model for feature 30
[longCombat] fitting lme model for feature 31 [longCombat] fitting lme
model for feature 32 [longCombat] fitting lme model for feature 33
[longCombat] fitting lme model for feature 34 [longCombat] fitting lme
model for feature 35 [longCombat] fitting lme model for feature 36
[longCombat] fitting lme model for feature 37 [longCombat] fitting lme
model for feature 38 [longCombat] fitting lme model for feature 39
[longCombat] fitting lme model for feature 40 [longCombat] fitting lme
model for feature 41 [longCombat] fitting lme model for feature 42
[longCombat] fitting lme model for feature 43 [longCombat] fitting lme
model for feature 44 [longCombat] fitting lme model for feature 45
[longCombat] fitting lme model for feature 46 [longCombat] fitting lme
model for feature 47 [longCombat] fitting lme model for feature 48
[longCombat] fitting lme model for feature 49 [longCombat] fitting lme
model for feature 50 [longCombat] fitting lme model for feature 51
[longCombat] fitting lme model for feature 52 [longCombat] fitting lme
model for feature 53 [longCombat] fitting lme model for feature 54
[longCombat] fitting lme model for feature 55 [longCombat] fitting lme
model for feature 56 [longCombat] fitting lme model for feature 57
[longCombat] fitting lme model for feature 58 [longCombat] fitting lme
model for feature 59 [longCombat] fitting lme model for feature 60
[longCombat] fitting lme model for feature 61 [longCombat] fitting lme
model for feature 62 [longCombat] fitting lme model for feature 63
[longCombat] fitting lme model for feature 64 [longCombat] fitting lme
model for feature 65 [longCombat] fitting lme model for feature 66
[longCombat] fitting lme model for feature 67 [longCombat] fitting lme
model for feature 68 [longCombat] fitting lme model for feature 69
[longCombat] fitting lme model for feature 70 [longCombat] fitting lme
model for feature 71 [longCombat] using method of moments to estimate
hyperparameters [longCombat] using empirical Bayes to estimate batch
effects… [longCombat] initializing… [longCombat] starting EM algorithm
iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat]
starting EM algorithm iteration 3 [longCombat] starting EM algorithm
iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat]
starting EM algorithm iteration 6 [longCombat] starting EM algorithm
iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat]
starting EM algorithm iteration 9 [longCombat] starting EM algorithm
iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
71 features [longCombat] found 6863 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] fitting lme model for
feature 43 [longCombat] fitting lme model for feature 44 [longCombat]
fitting lme model for feature 45 [longCombat] fitting lme model for
feature 46 [longCombat] fitting lme model for feature 47 [longCombat]
fitting lme model for feature 48 [longCombat] fitting lme model for
feature 49 [longCombat] fitting lme model for feature 50 [longCombat]
fitting lme model for feature 51 [longCombat] fitting lme model for
feature 52 [longCombat] fitting lme model for feature 53 [longCombat]
fitting lme model for feature 54 [longCombat] fitting lme model for
feature 55 [longCombat] fitting lme model for feature 56 [longCombat]
fitting lme model for feature 57 [longCombat] fitting lme model for
feature 58 [longCombat] fitting lme model for feature 59 [longCombat]
fitting lme model for feature 60 [longCombat] fitting lme model for
feature 61 [longCombat] fitting lme model for feature 62 [longCombat]
fitting lme model for feature 63 [longCombat] fitting lme model for
feature 64 [longCombat] fitting lme model for feature 65 [longCombat]
fitting lme model for feature 66 [longCombat] fitting lme model for
feature 67 [longCombat] fitting lme model for feature 68 [longCombat]
fitting lme model for feature 69 [longCombat] fitting lme model for
feature 70 [longCombat] fitting lme model for feature 71 [longCombat]
using method of moments to estimate hyperparameters [longCombat] using
empirical Bayes to estimate batch effects… [longCombat] initializing…
[longCombat] starting EM algorithm iteration 1 [longCombat] starting EM
algorithm iteration 2 [longCombat] starting EM algorithm iteration 3
[longCombat] starting EM algorithm iteration 4 [longCombat] starting EM
algorithm iteration 5 [longCombat] starting EM algorithm iteration 6
[longCombat] starting EM algorithm iteration 7 [longCombat] starting EM
algorithm iteration 8 [longCombat] starting EM algorithm iteration 9
[longCombat] starting EM algorithm iteration 10 [longCombat] starting EM
algorithm iteration 11 [longCombat] starting EM algorithm iteration 12
[longCombat] starting EM algorithm iteration 13 [longCombat] starting EM
algorithm iteration 14 [longCombat] starting EM algorithm iteration 15
[longCombat] starting EM algorithm iteration 16 [longCombat] starting EM
algorithm iteration 17 [longCombat] starting EM algorithm iteration 18
[longCombat] starting EM algorithm iteration 19 [longCombat] starting EM
algorithm iteration 20 [longCombat] starting EM algorithm iteration 21
[longCombat] starting EM algorithm iteration 22 [longCombat] starting EM
algorithm iteration 23 [longCombat] starting EM algorithm iteration 24
[longCombat] starting EM algorithm iteration 25 [longCombat] starting EM
algorithm iteration 26 [longCombat] starting EM algorithm iteration 27
[longCombat] starting EM algorithm iteration 28 [longCombat] starting EM
algorithm iteration 29 [longCombat] starting EM algorithm iteration 30
[longCombat] adjusting data for batch effects
Harmonization Model: age
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6
dataset: area | sex: m |
region: smri_area_cdk_mdtmlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
95185.14
|
95212.48
|
-47588.57
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
94902.78
|
94957.45
|
-47443.39
|
1 vs 2
|
290.3618
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
97129.24
|
97156.58
|
-48560.62
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
97137.02
|
97191.69
|
-48560.51
|
1 vs 2
|
0.2276452
|
0.9939934
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
3755.839
|
462.895
|
|
brain_metric
|
scanner2
|
1808
|
4037.319
|
486.071
|
|
brain_metric
|
scanner3
|
196
|
3646.485
|
514.494
|
|
brain_metric
|
scanner5
|
2016
|
3792.857
|
445.779
|
|
brain_metric
|
scanner6
|
2264
|
3833.975
|
449.369
|
|
brain_metric.combat
|
scanner1
|
579
|
3880.490
|
477.411
|
|
brain_metric.combat
|
scanner2
|
1808
|
3851.263
|
479.856
|
|
brain_metric.combat
|
scanner3
|
196
|
3866.100
|
526.370
|
|
brain_metric.combat
|
scanner5
|
2016
|
3843.197
|
467.669
|
|
brain_metric.combat
|
scanner6
|
2264
|
3886.699
|
469.732
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
6.835
|
|
|
|
|
age_slope
|
|
8.893
|
|
|
|
scanner:scanner3
|
3646.649
|
3878.281
|
231.632
|
6.352
|
|
scanner:scanner2
|
4051.359
|
3860.821
|
-190.538
|
-4.703
|
|
scanner:scanner1
|
3737.765
|
3864.961
|
127.196
|
3.403
|
|
scanner:scanner6
|
3812.994
|
3866.440
|
53.446
|
1.402
|
|
scanner:scanner5
|
3811.261
|
3863.904
|
52.644
|
1.381
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_timing
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled
dataset: area | sex: m |
region: smri_area_cdk_mdtmlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
95185.14
|
95212.48
|
-47588.57
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
94902.78
|
94957.45
|
-47443.39
|
1 vs 2
|
290.3618
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
97128.27
|
97155.60
|
-48560.13
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
97136.11
|
97190.78
|
-48560.05
|
1 vs 2
|
0.1589453
|
0.9970045
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
3755.839
|
462.895
|
|
brain_metric
|
scanner2
|
1808
|
4037.319
|
486.071
|
|
brain_metric
|
scanner3
|
196
|
3646.485
|
514.494
|
|
brain_metric
|
scanner5
|
2016
|
3792.857
|
445.779
|
|
brain_metric
|
scanner6
|
2264
|
3833.975
|
449.369
|
|
brain_metric.combat
|
scanner1
|
579
|
3883.229
|
477.432
|
|
brain_metric.combat
|
scanner2
|
1808
|
3850.960
|
479.852
|
|
brain_metric.combat
|
scanner3
|
196
|
3860.053
|
526.315
|
|
brain_metric.combat
|
scanner5
|
2016
|
3843.420
|
467.625
|
|
brain_metric.combat
|
scanner6
|
2264
|
3886.566
|
469.715
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
6.835
|
|
|
|
|
age_slope
|
|
8.915
|
|
|
|
scanner:scanner3
|
3646.649
|
3871.430
|
224.780
|
6.164
|
|
scanner:scanner2
|
4051.359
|
3860.500
|
-190.859
|
-4.711
|
|
scanner:scanner1
|
3737.765
|
3868.014
|
130.249
|
3.485
|
|
scanner:scanner6
|
3812.994
|
3866.167
|
53.173
|
1.395
|
|
scanner:scanner5
|
3811.261
|
3864.317
|
53.056
|
1.392
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_tempo
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled
dataset: area | sex: m |
region: smri_area_cdk_mdtmlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
95185.14
|
95212.48
|
-47588.57
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
94902.78
|
94957.45
|
-47443.39
|
1 vs 2
|
290.3618
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
97129.20
|
97156.54
|
-48560.60
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
97136.98
|
97191.65
|
-48560.49
|
1 vs 2
|
0.2211063
|
0.9943213
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
3755.839
|
462.895
|
|
brain_metric
|
scanner2
|
1808
|
4037.319
|
486.071
|
|
brain_metric
|
scanner3
|
196
|
3646.485
|
514.494
|
|
brain_metric
|
scanner5
|
2016
|
3792.857
|
445.779
|
|
brain_metric
|
scanner6
|
2264
|
3833.975
|
449.369
|
|
brain_metric.combat
|
scanner1
|
579
|
3880.744
|
477.413
|
|
brain_metric.combat
|
scanner2
|
1808
|
3850.934
|
479.853
|
|
brain_metric.combat
|
scanner3
|
196
|
3865.860
|
526.351
|
|
brain_metric.combat
|
scanner5
|
2016
|
3843.452
|
467.656
|
|
brain_metric.combat
|
scanner6
|
2264
|
3886.691
|
469.716
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
6.835
|
|
|
|
|
age_slope
|
|
8.899
|
|
|
|
scanner:scanner3
|
3646.649
|
3878.021
|
231.372
|
6.345
|
|
scanner:scanner2
|
4051.359
|
3860.472
|
-190.888
|
-4.712
|
|
scanner:scanner1
|
3737.765
|
3865.228
|
127.462
|
3.410
|
|
scanner:scanner6
|
3812.994
|
3866.326
|
53.332
|
1.399
|
|
scanner:scanner5
|
3811.261
|
3864.307
|
53.046
|
1.392
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_timing_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 +
s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 +
s_age_by_timing_6 + s_age_by_timing_7
dataset: area | sex: m |
region: smri_area_cdk_mdtmlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
95185.14
|
95212.48
|
-47588.57
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
94902.78
|
94957.45
|
-47443.39
|
1 vs 2
|
290.3618
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
97110.10
|
97137.43
|
-48551.05
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
97117.86
|
97172.53
|
-48550.93
|
1 vs 2
|
0.2442297
|
0.9931241
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
3755.839
|
462.895
|
|
brain_metric
|
scanner2
|
1808
|
4037.319
|
486.071
|
|
brain_metric
|
scanner3
|
196
|
3646.485
|
514.494
|
|
brain_metric
|
scanner5
|
2016
|
3792.857
|
445.779
|
|
brain_metric
|
scanner6
|
2264
|
3833.975
|
449.369
|
|
brain_metric.combat
|
scanner1
|
579
|
3883.105
|
477.438
|
|
brain_metric.combat
|
scanner2
|
1808
|
3850.890
|
479.864
|
|
brain_metric.combat
|
scanner3
|
196
|
3859.114
|
526.563
|
|
brain_metric.combat
|
scanner5
|
2016
|
3842.187
|
467.359
|
|
brain_metric.combat
|
scanner6
|
2264
|
3887.836
|
469.788
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
6.835
|
|
|
|
|
age_slope
|
|
8.892
|
|
|
|
scanner:scanner3
|
3646.649
|
3871.565
|
224.916
|
6.168
|
|
scanner:scanner2
|
4051.359
|
3860.576
|
-190.783
|
-4.709
|
|
scanner:scanner1
|
3737.765
|
3867.730
|
129.964
|
3.477
|
|
scanner:scanner6
|
3812.994
|
3867.089
|
54.095
|
1.419
|
|
scanner:scanner5
|
3811.261
|
3863.281
|
52.020
|
1.365
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_tempo_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 +
s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 +
s_age_by_tempo_6 + s_age_by_tempo_7
dataset: area | sex: m |
region: smri_area_cdk_mdtmlh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
95185.14
|
95212.48
|
-47588.57
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
94902.78
|
94957.45
|
-47443.39
|
1 vs 2
|
290.3618
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
97129.59
|
97156.92
|
-48560.79
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
97137.36
|
97192.03
|
-48560.68
|
1 vs 2
|
0.2306247
|
0.9938412
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
3755.839
|
462.895
|
|
brain_metric
|
scanner2
|
1808
|
4037.319
|
486.071
|
|
brain_metric
|
scanner3
|
196
|
3646.485
|
514.494
|
|
brain_metric
|
scanner5
|
2016
|
3792.857
|
445.779
|
|
brain_metric
|
scanner6
|
2264
|
3833.975
|
449.369
|
|
brain_metric.combat
|
scanner1
|
579
|
3880.444
|
477.540
|
|
brain_metric.combat
|
scanner2
|
1808
|
3850.944
|
479.892
|
|
brain_metric.combat
|
scanner3
|
196
|
3866.880
|
526.347
|
|
brain_metric.combat
|
scanner5
|
2016
|
3843.384
|
467.621
|
|
brain_metric.combat
|
scanner6
|
2264
|
3886.732
|
469.679
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
6.835
|
|
|
|
|
age_slope
|
|
8.888
|
|
|
|
scanner:scanner3
|
3646.649
|
3878.740
|
232.091
|
6.365
|
|
scanner:scanner2
|
4051.359
|
3860.543
|
-190.816
|
-4.710
|
|
scanner:scanner1
|
3737.765
|
3865.058
|
127.292
|
3.406
|
|
scanner:scanner6
|
3812.994
|
3866.321
|
53.328
|
1.399
|
|
scanner:scanner5
|
3811.261
|
3864.217
|
52.956
|
1.389
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
volume — f
## longcombat is dropping 2 feature(s) with NA.
[longCombat] found 5 batches [longCombat] found 42 features
[longCombat] found 6004 total observations [longCombat] standardizing
data across features… [longCombat] fitting lme model for feature 1
[longCombat] fitting lme model for feature 2 [longCombat] fitting lme
model for feature 3 [longCombat] fitting lme model for feature 4
[longCombat] fitting lme model for feature 5 [longCombat] fitting lme
model for feature 6 [longCombat] fitting lme model for feature 7
[longCombat] fitting lme model for feature 8 [longCombat] fitting lme
model for feature 9 [longCombat] fitting lme model for feature 10
[longCombat] fitting lme model for feature 11 [longCombat] fitting lme
model for feature 12 [longCombat] fitting lme model for feature 13
[longCombat] fitting lme model for feature 14 [longCombat] fitting lme
model for feature 15 [longCombat] fitting lme model for feature 16
[longCombat] fitting lme model for feature 17 [longCombat] fitting lme
model for feature 18 [longCombat] fitting lme model for feature 19
[longCombat] fitting lme model for feature 20 [longCombat] fitting lme
model for feature 21 [longCombat] fitting lme model for feature 22
[longCombat] fitting lme model for feature 23 [longCombat] fitting lme
model for feature 24 [longCombat] fitting lme model for feature 25
[longCombat] fitting lme model for feature 26 [longCombat] fitting lme
model for feature 27 [longCombat] fitting lme model for feature 28
[longCombat] fitting lme model for feature 29 [longCombat] fitting lme
model for feature 30 [longCombat] fitting lme model for feature 31
[longCombat] fitting lme model for feature 32 [longCombat] fitting lme
model for feature 33 [longCombat] fitting lme model for feature 34
[longCombat] fitting lme model for feature 35 [longCombat] fitting lme
model for feature 36 [longCombat] fitting lme model for feature 37
[longCombat] fitting lme model for feature 38 [longCombat] fitting lme
model for feature 39 [longCombat] fitting lme model for feature 40
[longCombat] fitting lme model for feature 41 [longCombat] fitting lme
model for feature 42 [longCombat] using method of moments to estimate
hyperparameters [longCombat] using empirical Bayes to estimate batch
effects… [longCombat] initializing… [longCombat] starting EM algorithm
iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat]
starting EM algorithm iteration 3 [longCombat] starting EM algorithm
iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat]
starting EM algorithm iteration 6 [longCombat] starting EM algorithm
iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat]
starting EM algorithm iteration 9 [longCombat] starting EM algorithm
iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
42 features [longCombat] found 6004 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] using method of moments to
estimate hyperparameters [longCombat] using empirical Bayes to estimate
batch effects… [longCombat] initializing… [longCombat] starting EM
algorithm iteration 1 [longCombat] starting EM algorithm iteration 2
[longCombat] starting EM algorithm iteration 3 [longCombat] starting EM
algorithm iteration 4 [longCombat] starting EM algorithm iteration 5
[longCombat] starting EM algorithm iteration 6 [longCombat] starting EM
algorithm iteration 7 [longCombat] starting EM algorithm iteration 8
[longCombat] starting EM algorithm iteration 9 [longCombat] starting EM
algorithm iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
42 features [longCombat] found 6004 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] using method of moments to
estimate hyperparameters [longCombat] using empirical Bayes to estimate
batch effects… [longCombat] initializing… [longCombat] starting EM
algorithm iteration 1 [longCombat] starting EM algorithm iteration 2
[longCombat] starting EM algorithm iteration 3 [longCombat] starting EM
algorithm iteration 4 [longCombat] starting EM algorithm iteration 5
[longCombat] starting EM algorithm iteration 6 [longCombat] starting EM
algorithm iteration 7 [longCombat] starting EM algorithm iteration 8
[longCombat] starting EM algorithm iteration 9 [longCombat] starting EM
algorithm iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
42 features [longCombat] found 6004 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] using method of moments to
estimate hyperparameters [longCombat] using empirical Bayes to estimate
batch effects… [longCombat] initializing… [longCombat] starting EM
algorithm iteration 1 [longCombat] starting EM algorithm iteration 2
[longCombat] starting EM algorithm iteration 3 [longCombat] starting EM
algorithm iteration 4 [longCombat] starting EM algorithm iteration 5
[longCombat] starting EM algorithm iteration 6 [longCombat] starting EM
algorithm iteration 7 [longCombat] starting EM algorithm iteration 8
[longCombat] starting EM algorithm iteration 9 [longCombat] starting EM
algorithm iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
42 features [longCombat] found 6004 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] using method of moments to
estimate hyperparameters [longCombat] using empirical Bayes to estimate
batch effects… [longCombat] initializing… [longCombat] starting EM
algorithm iteration 1 [longCombat] starting EM algorithm iteration 2
[longCombat] starting EM algorithm iteration 3 [longCombat] starting EM
algorithm iteration 4 [longCombat] starting EM algorithm iteration 5
[longCombat] starting EM algorithm iteration 6 [longCombat] starting EM
algorithm iteration 7 [longCombat] starting EM algorithm iteration 8
[longCombat] starting EM algorithm iteration 9 [longCombat] starting EM
algorithm iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects
Harmonization Model: age
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6
dataset: volume | sex: f |
region: smri_vol_scs_amygdalarh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
75803.15
|
75829.95
|
-37897.57
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
75746.13
|
75799.73
|
-37865.07
|
1 vs 2
|
65.01632
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
77717.92
|
77744.72
|
-38854.96
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
77725.84
|
77779.45
|
-38854.92
|
1 vs 2
|
0.0717721
|
0.9993713
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
1792.106
|
188.323
|
|
brain_metric
|
scanner2
|
1720
|
1726.334
|
191.118
|
|
brain_metric
|
scanner3
|
204
|
1781.268
|
183.449
|
|
brain_metric
|
scanner5
|
1596
|
1782.956
|
188.630
|
|
brain_metric
|
scanner6
|
2015
|
1775.741
|
188.518
|
|
brain_metric.combat
|
scanner1
|
469
|
1772.467
|
197.977
|
|
brain_metric.combat
|
scanner2
|
1720
|
1767.412
|
200.762
|
|
brain_metric.combat
|
scanner3
|
204
|
1765.441
|
190.134
|
|
brain_metric.combat
|
scanner5
|
1596
|
1760.986
|
198.590
|
|
brain_metric.combat
|
scanner6
|
2015
|
1764.321
|
198.678
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
8.034
|
|
|
|
|
age_slope
|
|
8.112
|
|
|
|
scanner:scanner2
|
1723.371
|
1764.659
|
41.288
|
2.396
|
|
scanner:scanner5
|
1785.131
|
1763.086
|
-22.045
|
-1.235
|
|
scanner:scanner1
|
1783.755
|
1766.018
|
-17.736
|
-0.994
|
|
scanner:scanner3
|
1780.944
|
1765.092
|
-15.852
|
-0.890
|
|
scanner:scanner6
|
1776.311
|
1764.380
|
-11.931
|
-0.672
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_timing
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled
dataset: volume | sex: f |
region: smri_vol_scs_amygdalarh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
75803.15
|
75829.95
|
-37897.57
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
75746.13
|
75799.73
|
-37865.07
|
1 vs 2
|
65.01632
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
77717.62
|
77744.42
|
-38854.81
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
77725.55
|
77779.15
|
-38854.77
|
1 vs 2
|
0.0698744
|
0.9994037
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
1792.106
|
188.323
|
|
brain_metric
|
scanner2
|
1720
|
1726.334
|
191.118
|
|
brain_metric
|
scanner3
|
204
|
1781.268
|
183.449
|
|
brain_metric
|
scanner5
|
1596
|
1782.956
|
188.630
|
|
brain_metric
|
scanner6
|
2015
|
1775.741
|
188.518
|
|
brain_metric.combat
|
scanner1
|
469
|
1773.020
|
197.982
|
|
brain_metric.combat
|
scanner2
|
1720
|
1767.292
|
200.756
|
|
brain_metric.combat
|
scanner3
|
204
|
1764.442
|
190.136
|
|
brain_metric.combat
|
scanner5
|
1596
|
1761.164
|
198.585
|
|
brain_metric.combat
|
scanner6
|
2015
|
1764.255
|
198.678
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
8.034
|
|
|
|
|
age_slope
|
|
8.125
|
|
|
|
scanner:scanner2
|
1723.371
|
1764.540
|
41.170
|
2.389
|
|
scanner:scanner5
|
1785.131
|
1763.285
|
-21.846
|
-1.224
|
|
scanner:scanner1
|
1783.755
|
1766.584
|
-17.170
|
-0.963
|
|
scanner:scanner3
|
1780.944
|
1764.081
|
-16.863
|
-0.947
|
|
scanner:scanner6
|
1776.311
|
1764.301
|
-12.010
|
-0.676
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_tempo
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled
dataset: volume | sex: f |
region: smri_vol_scs_amygdalarh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
75803.15
|
75829.95
|
-37897.57
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
75746.13
|
75799.73
|
-37865.07
|
1 vs 2
|
65.01632
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
77717.43
|
77744.23
|
-38854.71
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
77725.33
|
77778.93
|
-38854.67
|
1 vs 2
|
0.0946972
|
0.9989138
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
1792.106
|
188.323
|
|
brain_metric
|
scanner2
|
1720
|
1726.334
|
191.118
|
|
brain_metric
|
scanner3
|
204
|
1781.268
|
183.449
|
|
brain_metric
|
scanner5
|
1596
|
1782.956
|
188.630
|
|
brain_metric
|
scanner6
|
2015
|
1775.741
|
188.518
|
|
brain_metric.combat
|
scanner1
|
469
|
1773.329
|
197.979
|
|
brain_metric.combat
|
scanner2
|
1720
|
1767.248
|
200.755
|
|
brain_metric.combat
|
scanner3
|
204
|
1764.267
|
190.132
|
|
brain_metric.combat
|
scanner5
|
1596
|
1761.015
|
198.579
|
|
brain_metric.combat
|
scanner6
|
2015
|
1764.356
|
198.682
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
8.034
|
|
|
|
|
age_slope
|
|
8.119
|
|
|
|
scanner:scanner2
|
1723.371
|
1764.496
|
41.125
|
2.386
|
|
scanner:scanner5
|
1785.131
|
1763.112
|
-22.019
|
-1.233
|
|
scanner:scanner3
|
1780.944
|
1763.903
|
-17.041
|
-0.957
|
|
scanner:scanner1
|
1783.755
|
1766.906
|
-16.849
|
-0.945
|
|
scanner:scanner6
|
1776.311
|
1764.413
|
-11.898
|
-0.670
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_timing_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 +
s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 +
s_age_by_timing_6 + s_age_by_timing_7
dataset: volume | sex: f |
region: smri_vol_scs_amygdalarh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
75803.15
|
75829.95
|
-37897.57
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
75746.13
|
75799.73
|
-37865.07
|
1 vs 2
|
65.01632
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
77708.42
|
77735.22
|
-38850.21
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
77716.31
|
77769.91
|
-38850.16
|
1 vs 2
|
0.1074109
|
0.9986085
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
1792.106
|
188.323
|
|
brain_metric
|
scanner2
|
1720
|
1726.334
|
191.118
|
|
brain_metric
|
scanner3
|
204
|
1781.268
|
183.449
|
|
brain_metric
|
scanner5
|
1596
|
1782.956
|
188.630
|
|
brain_metric
|
scanner6
|
2015
|
1775.741
|
188.518
|
|
brain_metric.combat
|
scanner1
|
469
|
1772.498
|
197.869
|
|
brain_metric.combat
|
scanner2
|
1720
|
1767.195
|
200.603
|
|
brain_metric.combat
|
scanner3
|
204
|
1764.090
|
190.516
|
|
brain_metric.combat
|
scanner5
|
1596
|
1760.712
|
198.598
|
|
brain_metric.combat
|
scanner6
|
2015
|
1764.851
|
198.622
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
8.034
|
|
|
|
|
age_slope
|
|
8.107
|
|
|
|
scanner:scanner2
|
1723.371
|
1764.450
|
41.079
|
2.384
|
|
scanner:scanner5
|
1785.131
|
1762.874
|
-22.257
|
-1.247
|
|
scanner:scanner1
|
1783.755
|
1766.097
|
-17.658
|
-0.990
|
|
scanner:scanner3
|
1780.944
|
1763.712
|
-17.231
|
-0.968
|
|
scanner:scanner6
|
1776.311
|
1764.849
|
-11.462
|
-0.645
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_tempo_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 +
s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 +
s_age_by_tempo_6 + s_age_by_tempo_7
dataset: volume | sex: f |
region: smri_vol_scs_amygdalarh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
75803.15
|
75829.95
|
-37897.57
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
75746.13
|
75799.73
|
-37865.07
|
1 vs 2
|
65.01632
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
77722.00
|
77748.81
|
-38857.00
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
77729.91
|
77783.51
|
-38856.95
|
1 vs 2
|
0.0968784
|
0.998864
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
469
|
1792.106
|
188.323
|
|
brain_metric
|
scanner2
|
1720
|
1726.334
|
191.118
|
|
brain_metric
|
scanner3
|
204
|
1781.268
|
183.449
|
|
brain_metric
|
scanner5
|
1596
|
1782.956
|
188.630
|
|
brain_metric
|
scanner6
|
2015
|
1775.741
|
188.518
|
|
brain_metric.combat
|
scanner1
|
469
|
1773.401
|
198.054
|
|
brain_metric.combat
|
scanner2
|
1720
|
1767.257
|
200.774
|
|
brain_metric.combat
|
scanner3
|
204
|
1764.213
|
190.099
|
|
brain_metric.combat
|
scanner5
|
1596
|
1761.009
|
198.602
|
|
brain_metric.combat
|
scanner6
|
2015
|
1764.341
|
198.732
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
8.034
|
|
|
|
|
age_slope
|
|
8.120
|
|
|
|
scanner:scanner2
|
1723.371
|
1764.508
|
41.137
|
2.387
|
|
scanner:scanner5
|
1785.131
|
1763.110
|
-22.021
|
-1.234
|
|
scanner:scanner3
|
1780.944
|
1763.856
|
-17.088
|
-0.960
|
|
scanner:scanner1
|
1783.755
|
1766.966
|
-16.788
|
-0.941
|
|
scanner:scanner6
|
1776.311
|
1764.395
|
-11.916
|
-0.671
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
volume — m
## longcombat is dropping 2 feature(s) with NA.
[longCombat] found 5 batches [longCombat] found 42 features
[longCombat] found 6863 total observations [longCombat] standardizing
data across features… [longCombat] fitting lme model for feature 1
[longCombat] fitting lme model for feature 2 [longCombat] fitting lme
model for feature 3 [longCombat] fitting lme model for feature 4
[longCombat] fitting lme model for feature 5 [longCombat] fitting lme
model for feature 6 [longCombat] fitting lme model for feature 7
[longCombat] fitting lme model for feature 8 [longCombat] fitting lme
model for feature 9 [longCombat] fitting lme model for feature 10
[longCombat] fitting lme model for feature 11 [longCombat] fitting lme
model for feature 12 [longCombat] fitting lme model for feature 13
[longCombat] fitting lme model for feature 14 [longCombat] fitting lme
model for feature 15 [longCombat] fitting lme model for feature 16
[longCombat] fitting lme model for feature 17 [longCombat] fitting lme
model for feature 18 [longCombat] fitting lme model for feature 19
[longCombat] fitting lme model for feature 20 [longCombat] fitting lme
model for feature 21 [longCombat] fitting lme model for feature 22
[longCombat] fitting lme model for feature 23 [longCombat] fitting lme
model for feature 24 [longCombat] fitting lme model for feature 25
[longCombat] fitting lme model for feature 26 [longCombat] fitting lme
model for feature 27 [longCombat] fitting lme model for feature 28
[longCombat] fitting lme model for feature 29 [longCombat] fitting lme
model for feature 30 [longCombat] fitting lme model for feature 31
[longCombat] fitting lme model for feature 32 [longCombat] fitting lme
model for feature 33 [longCombat] fitting lme model for feature 34
[longCombat] fitting lme model for feature 35 [longCombat] fitting lme
model for feature 36 [longCombat] fitting lme model for feature 37
[longCombat] fitting lme model for feature 38 [longCombat] fitting lme
model for feature 39 [longCombat] fitting lme model for feature 40
[longCombat] fitting lme model for feature 41 [longCombat] fitting lme
model for feature 42 [longCombat] using method of moments to estimate
hyperparameters [longCombat] using empirical Bayes to estimate batch
effects… [longCombat] initializing… [longCombat] starting EM algorithm
iteration 1 [longCombat] starting EM algorithm iteration 2 [longCombat]
starting EM algorithm iteration 3 [longCombat] starting EM algorithm
iteration 4 [longCombat] starting EM algorithm iteration 5 [longCombat]
starting EM algorithm iteration 6 [longCombat] starting EM algorithm
iteration 7 [longCombat] starting EM algorithm iteration 8 [longCombat]
starting EM algorithm iteration 9 [longCombat] starting EM algorithm
iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
42 features [longCombat] found 6863 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] using method of moments to
estimate hyperparameters [longCombat] using empirical Bayes to estimate
batch effects… [longCombat] initializing… [longCombat] starting EM
algorithm iteration 1 [longCombat] starting EM algorithm iteration 2
[longCombat] starting EM algorithm iteration 3 [longCombat] starting EM
algorithm iteration 4 [longCombat] starting EM algorithm iteration 5
[longCombat] starting EM algorithm iteration 6 [longCombat] starting EM
algorithm iteration 7 [longCombat] starting EM algorithm iteration 8
[longCombat] starting EM algorithm iteration 9 [longCombat] starting EM
algorithm iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
42 features [longCombat] found 6863 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] using method of moments to
estimate hyperparameters [longCombat] using empirical Bayes to estimate
batch effects… [longCombat] initializing… [longCombat] starting EM
algorithm iteration 1 [longCombat] starting EM algorithm iteration 2
[longCombat] starting EM algorithm iteration 3 [longCombat] starting EM
algorithm iteration 4 [longCombat] starting EM algorithm iteration 5
[longCombat] starting EM algorithm iteration 6 [longCombat] starting EM
algorithm iteration 7 [longCombat] starting EM algorithm iteration 8
[longCombat] starting EM algorithm iteration 9 [longCombat] starting EM
algorithm iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
42 features [longCombat] found 6863 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] using method of moments to
estimate hyperparameters [longCombat] using empirical Bayes to estimate
batch effects… [longCombat] initializing… [longCombat] starting EM
algorithm iteration 1 [longCombat] starting EM algorithm iteration 2
[longCombat] starting EM algorithm iteration 3 [longCombat] starting EM
algorithm iteration 4 [longCombat] starting EM algorithm iteration 5
[longCombat] starting EM algorithm iteration 6 [longCombat] starting EM
algorithm iteration 7 [longCombat] starting EM algorithm iteration 8
[longCombat] starting EM algorithm iteration 9 [longCombat] starting EM
algorithm iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects [longCombat] found 5 batches [longCombat] found
42 features [longCombat] found 6863 total observations [longCombat]
standardizing data across features… [longCombat] fitting lme model for
feature 1 [longCombat] fitting lme model for feature 2 [longCombat]
fitting lme model for feature 3 [longCombat] fitting lme model for
feature 4 [longCombat] fitting lme model for feature 5 [longCombat]
fitting lme model for feature 6 [longCombat] fitting lme model for
feature 7 [longCombat] fitting lme model for feature 8 [longCombat]
fitting lme model for feature 9 [longCombat] fitting lme model for
feature 10 [longCombat] fitting lme model for feature 11 [longCombat]
fitting lme model for feature 12 [longCombat] fitting lme model for
feature 13 [longCombat] fitting lme model for feature 14 [longCombat]
fitting lme model for feature 15 [longCombat] fitting lme model for
feature 16 [longCombat] fitting lme model for feature 17 [longCombat]
fitting lme model for feature 18 [longCombat] fitting lme model for
feature 19 [longCombat] fitting lme model for feature 20 [longCombat]
fitting lme model for feature 21 [longCombat] fitting lme model for
feature 22 [longCombat] fitting lme model for feature 23 [longCombat]
fitting lme model for feature 24 [longCombat] fitting lme model for
feature 25 [longCombat] fitting lme model for feature 26 [longCombat]
fitting lme model for feature 27 [longCombat] fitting lme model for
feature 28 [longCombat] fitting lme model for feature 29 [longCombat]
fitting lme model for feature 30 [longCombat] fitting lme model for
feature 31 [longCombat] fitting lme model for feature 32 [longCombat]
fitting lme model for feature 33 [longCombat] fitting lme model for
feature 34 [longCombat] fitting lme model for feature 35 [longCombat]
fitting lme model for feature 36 [longCombat] fitting lme model for
feature 37 [longCombat] fitting lme model for feature 38 [longCombat]
fitting lme model for feature 39 [longCombat] fitting lme model for
feature 40 [longCombat] fitting lme model for feature 41 [longCombat]
fitting lme model for feature 42 [longCombat] using method of moments to
estimate hyperparameters [longCombat] using empirical Bayes to estimate
batch effects… [longCombat] initializing… [longCombat] starting EM
algorithm iteration 1 [longCombat] starting EM algorithm iteration 2
[longCombat] starting EM algorithm iteration 3 [longCombat] starting EM
algorithm iteration 4 [longCombat] starting EM algorithm iteration 5
[longCombat] starting EM algorithm iteration 6 [longCombat] starting EM
algorithm iteration 7 [longCombat] starting EM algorithm iteration 8
[longCombat] starting EM algorithm iteration 9 [longCombat] starting EM
algorithm iteration 10 [longCombat] starting EM algorithm iteration 11
[longCombat] starting EM algorithm iteration 12 [longCombat] starting EM
algorithm iteration 13 [longCombat] starting EM algorithm iteration 14
[longCombat] starting EM algorithm iteration 15 [longCombat] starting EM
algorithm iteration 16 [longCombat] starting EM algorithm iteration 17
[longCombat] starting EM algorithm iteration 18 [longCombat] starting EM
algorithm iteration 19 [longCombat] starting EM algorithm iteration 20
[longCombat] starting EM algorithm iteration 21 [longCombat] starting EM
algorithm iteration 22 [longCombat] starting EM algorithm iteration 23
[longCombat] starting EM algorithm iteration 24 [longCombat] starting EM
algorithm iteration 25 [longCombat] starting EM algorithm iteration 26
[longCombat] starting EM algorithm iteration 27 [longCombat] starting EM
algorithm iteration 28 [longCombat] starting EM algorithm iteration 29
[longCombat] starting EM algorithm iteration 30 [longCombat] adjusting
data for batch effects
Harmonization Model: age
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6
dataset: volume | sex: m |
region: smri_vol_scs_amygdalarh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
87765.12
|
87792.46
|
-43878.56
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
87676.81
|
87731.48
|
-43830.40
|
1 vs 2
|
96.31319
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
89945.34
|
89972.68
|
-44968.67
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
89953.18
|
90007.85
|
-44968.59
|
1 vs 2
|
0.1670111
|
0.9967016
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
1957.793
|
226.961
|
|
brain_metric
|
scanner2
|
1808
|
1887.890
|
210.181
|
|
brain_metric
|
scanner3
|
196
|
1924.909
|
208.734
|
|
brain_metric
|
scanner5
|
2016
|
1951.739
|
206.193
|
|
brain_metric
|
scanner6
|
2264
|
1953.379
|
211.401
|
|
brain_metric.combat
|
scanner1
|
579
|
1926.908
|
234.271
|
|
brain_metric.combat
|
scanner2
|
1808
|
1940.706
|
223.463
|
|
brain_metric.combat
|
scanner3
|
196
|
1944.029
|
215.666
|
|
brain_metric.combat
|
scanner5
|
2016
|
1928.233
|
215.624
|
|
brain_metric.combat
|
scanner6
|
2264
|
1938.445
|
221.035
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
21.828
|
|
|
|
|
age_slope
|
|
21.880
|
|
|
|
scanner:scanner2
|
1881.675
|
1935.396
|
53.721
|
2.855
|
|
scanner:scanner1
|
1965.306
|
1932.062
|
-33.243
|
-1.692
|
|
scanner:scanner5
|
1957.986
|
1933.281
|
-24.705
|
-1.262
|
|
scanner:scanner3
|
1914.673
|
1934.310
|
19.637
|
1.026
|
|
scanner:scanner6
|
1949.493
|
1935.556
|
-13.938
|
-0.715
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_timing
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + timing_parent_scaled
dataset: volume | sex: m |
region: smri_vol_scs_amygdalarh


Distributions

Harmonization Model: age_tempo
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + tempo_parent_scaled
dataset: volume | sex: m |
region: smri_vol_scs_amygdalarh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
87765.12
|
87792.46
|
-43878.56
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
87676.81
|
87731.48
|
-43830.40
|
1 vs 2
|
96.31319
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
89945.36
|
89972.69
|
-44968.68
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
89953.22
|
90007.89
|
-44968.61
|
1 vs 2
|
0.1416313
|
0.9976079
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
1957.793
|
226.961
|
|
brain_metric
|
scanner2
|
1808
|
1887.890
|
210.181
|
|
brain_metric
|
scanner3
|
196
|
1924.909
|
208.734
|
|
brain_metric
|
scanner5
|
2016
|
1951.739
|
206.193
|
|
brain_metric
|
scanner6
|
2264
|
1953.379
|
211.401
|
|
brain_metric.combat
|
scanner1
|
579
|
1926.996
|
234.273
|
|
brain_metric.combat
|
scanner2
|
1808
|
1940.587
|
223.461
|
|
brain_metric.combat
|
scanner3
|
196
|
1943.951
|
215.669
|
|
brain_metric.combat
|
scanner5
|
2016
|
1928.369
|
215.626
|
|
brain_metric.combat
|
scanner6
|
2264
|
1938.403
|
221.032
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
21.828
|
|
|
|
|
age_slope
|
|
21.882
|
|
|
|
scanner:scanner2
|
1881.675
|
1935.275
|
53.600
|
2.849
|
|
scanner:scanner1
|
1965.306
|
1932.151
|
-33.155
|
-1.687
|
|
scanner:scanner5
|
1957.986
|
1933.432
|
-24.554
|
-1.254
|
|
scanner:scanner3
|
1914.673
|
1934.237
|
19.564
|
1.022
|
|
scanner:scanner6
|
1949.493
|
1935.505
|
-13.989
|
-0.718
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_timing_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + s_age_by_timing_1 + s_age_by_timing_2 +
s_age_by_timing_3 + s_age_by_timing_4 + s_age_by_timing_5 +
s_age_by_timing_6 + s_age_by_timing_7
dataset: volume | sex: m |
region: smri_vol_scs_amygdalarh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
87765.12
|
87792.46
|
-43878.56
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
87676.81
|
87731.48
|
-43830.40
|
1 vs 2
|
96.31319
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
89945.38
|
89972.71
|
-44968.69
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
89953.18
|
90007.85
|
-44968.59
|
1 vs 2
|
0.1991848
|
0.995358
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
1957.793
|
226.961
|
|
brain_metric
|
scanner2
|
1808
|
1887.890
|
210.181
|
|
brain_metric
|
scanner3
|
196
|
1924.909
|
208.734
|
|
brain_metric
|
scanner5
|
2016
|
1951.739
|
206.193
|
|
brain_metric
|
scanner6
|
2264
|
1953.379
|
211.401
|
|
brain_metric.combat
|
scanner1
|
579
|
1927.357
|
234.012
|
|
brain_metric.combat
|
scanner2
|
1808
|
1940.684
|
223.418
|
|
brain_metric.combat
|
scanner3
|
196
|
1942.564
|
216.057
|
|
brain_metric.combat
|
scanner5
|
2016
|
1928.022
|
215.698
|
|
brain_metric.combat
|
scanner6
|
2264
|
1938.660
|
221.047
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
21.828
|
|
|
|
|
age_slope
|
|
21.871
|
|
|
|
scanner:scanner2
|
1881.675
|
1935.395
|
53.720
|
2.855
|
|
scanner:scanner1
|
1965.306
|
1932.469
|
-32.836
|
-1.671
|
|
scanner:scanner5
|
1957.986
|
1933.091
|
-24.895
|
-1.271
|
|
scanner:scanner3
|
1914.673
|
1932.895
|
18.222
|
0.952
|
|
scanner:scanner6
|
1949.493
|
1935.743
|
-13.751
|
-0.705
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|
Harmonization Model: age_tempo_interaction
longCombat formula: s_age_1 + s_age_2 + s_age_3 +
s_age_4 + s_age_5 + s_age_6 + s_age_by_tempo_1 + s_age_by_tempo_2 +
s_age_by_tempo_3 + s_age_by_tempo_4 + s_age_by_tempo_5 +
s_age_by_tempo_6 + s_age_by_tempo_7
dataset: volume | sex: m |
region: smri_vol_scs_amygdalarh


Distributions

Scanner LRTs
LRT before harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0
|
lme.formula(fixed = brain_metric ~ age_scaled, data = mod_dat, random =
~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
87765.12
|
87792.46
|
-43878.56
|
|
|
|
|
m1
|
lme.formula(fixed = brain_metric ~ age_scaled + scanner, data = mod_dat,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
87676.81
|
87731.48
|
-43830.40
|
1 vs 2
|
96.31319
|
0
|
LRT after harmonization
|
|
call
|
Model
|
df
|
AIC
|
BIC
|
logLik
|
Test
|
L.Ratio
|
p-value
|
|
m0c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled, data = mod_datc,
random = ~1 | id, method = “ML”, na.action = na.omit)
|
1
|
4
|
89947.24
|
89974.58
|
-44969.62
|
|
|
|
|
m1c
|
lme.formula(fixed = brain_metric.combat ~ age_scaled + scanner, data =
mod_datc, random = ~1 | id, method = “ML”, na.action = na.omit)
|
2
|
8
|
89955.08
|
90009.75
|
-44969.54
|
1 vs 2
|
0.1606397
|
0.996942
|
QC Plots and Summaries
ROI summary by scanner (before vs after)
|
pipeline
|
scanner
|
n
|
mean
|
sd
|
|
brain_metric
|
scanner1
|
579
|
1957.793
|
226.961
|
|
brain_metric
|
scanner2
|
1808
|
1887.890
|
210.181
|
|
brain_metric
|
scanner3
|
196
|
1924.909
|
208.734
|
|
brain_metric
|
scanner5
|
2016
|
1951.739
|
206.193
|
|
brain_metric
|
scanner6
|
2264
|
1953.379
|
211.401
|
|
brain_metric.combat
|
scanner1
|
579
|
1927.115
|
234.325
|
|
brain_metric.combat
|
scanner2
|
1808
|
1940.618
|
223.493
|
|
brain_metric.combat
|
scanner3
|
196
|
1943.393
|
215.638
|
|
brain_metric.combat
|
scanner5
|
2016
|
1928.239
|
215.659
|
|
brain_metric.combat
|
scanner6
|
2264
|
1938.511
|
221.006
|
Fixed effects change (pre vs post)
|
kind
|
Value_pre
|
Value_post
|
diff
|
pctchg
|
|
age_slope
|
21.828
|
|
|
|
|
age_slope
|
|
21.885
|
|
|
|
scanner:scanner2
|
1881.675
|
1935.280
|
53.605
|
2.849
|
|
scanner:scanner1
|
1965.306
|
1932.279
|
-33.027
|
-1.680
|
|
scanner:scanner5
|
1957.986
|
1933.311
|
-24.675
|
-1.260
|
|
scanner:scanner3
|
1914.673
|
1933.715
|
19.043
|
0.995
|
|
scanner:scanner6
|
1949.493
|
1935.621
|
-13.873
|
-0.712
|
|
scanner:scanner4
|
|
|
|
|
|
scanner:scanner7
|
|
|
|
|
|
scanner:scanner8
|
|
|
|
|